Joint and cartilage diagnosis, assessment and modeling

ABSTRACT

Methods are disclosed for assessing the condition of a cartilage in a joint and assessing cartilage loss, particularly in a human knee. The methods include converting an image such as an MRI to a three dimensional map of the cartilage. The cartilage map can be correlated to a movement pattern of the joint to assess the affect of movement on cartilage wear. Changes in the thickness of cartilage over time can be determined so that therapies can be provided. The amount of cartilage tissue that has been lost, for example as a result of arthritis, can be estimated.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent applicationSer. No. 09/953,373, entitled “Assessing the Condition of a Joint andAssessing Cartilage Loss,” filed Sep. 14, 2001, which in turn claims thebenefit of U.S. Patent Application No. 60/232,637 and 60/232,639, bothfiled on Sep. 14, 2000. Each of these patent applications described inthis paragraph is hereby incorporated by reference, in its entirety.

TECHNICAL FIELD

This invention relates to assessing the condition of a joint and the useof the assessment in aiding in prevention of damage to the joint ortreatment of diseased cartilage in the joint.

BACKGROUND ART

Osteoarthritis is the most common condition to affect human joints aswell as a frequent cause of locomotor pain and disability. Moreparticularly, osteoarthritis (OA) of the knee occurs in a substantialportion of the population over the age of fifty.

In spite of its societal impact and prevalence, however, there is apaucity of information on the factors that cause osteoarthritis toprogress more rapidly in some individuals and not in others. Previouslyconsidered a “wear and tear” degenerative disease with littleopportunity for therapeutic intervention, osteoarthritis is nowincreasingly viewed as a dynamic process with potential for newpharmacologic and surgical treatment modalites such as cartilagetransplantation, osteochondral allo- or autografting, osteotomies andtibial corticotomies with angular distraction.

However, the appropriate deployment and selection of treatmentinterventions for OA is dependent on the development of better methodsfor the assessment of the condition of a patient's joint and thedegeneration process.

There is, therefore, a need for improved methods for examining thefactors that influence as well as quantification of the progression ofthe disease.

Magnetic resonance imaging (MRI) is an accurate non-invasive imagingtechnique for visualization of articular cartilage in osteoarthritis,particularly in knees. However, current MRI techniques cannot provideinformation on the relationship between the location of the cartilageloss and variations in the load bearing areas during the walking cycle.This information is important since it has been shown that dynamic loadsduring walking are related to the progression of knee OA. Thus, theability to locate cartilage defects or areas of cartilage thinningrelative to the load bearing areas of the knee could be valuable inevaluating factors influencing the progression of osteoarthritis.

REFERENCES

-   1. Alexander E J: Estimating the motion of bones from markers on the    skin [Doctoral Dissertation]. University of Illinois at Chicago;    1998.-   2. Alexander E J, Andriacchi T P: Correcting for deformation in    skin-based marker systems. Proceedings of the 3rd Annual Gait and    Clinical Movement Analysis Meeting, San Diego, Calif., 1998.-   3. Alexander E J, Andriacchi T P: Internal to external    correspondence in the analysis of lower limb bone motion.    Proceedings of the 1999 ASME Summer Bioengineering Conference, Big    Sky, Mont., 1999.-   4. Alexander E J, Andriacchi T P: State estimation theory in human    movement analysis. Proceedings of the 1998 ASME International    Mechanical Engineering Congress, 1998.-   5. Alexander E J, Andriacchi T P, Lang P K: Dynamic functional    imaging of the musculoskeletal system. ASME Winter International    Congress and Exposition, Nashville, Term., 1999.-   6. Alexander E J, Andriacchi T P, Naylor D L: Optimization    techniques for skin deformation correction. International Symposium    on 3-D Human Movement Conference, Chattanooga, Term., 1998.-   7. Allen P R, Denham R A, Swan A V: Late degenerative changes after    meniscectomy: factors affecting the knee after operations. J Bone    Joint Surg 1984; 66B: 666-671.-   8. Alley M T, Shifrin R Y, Pelc N J, Herfkens R J: Ultrafast    contrast-enhanced three dimensional MR angiography: state of the    art. Radiographics 1998; 18: 273-285.-   9. Andriacchi T P: Dynamics of knee malalignment. Orthop Clin North    Am 1994; 25: 395-403.-   10. Andriacchi T P, Alexander E J, Toney M K, Dyrby C O, Sum J: A    point cluster method for in vivo motion analysis: applied to a study    of knee kinematics. J Biomech Eng 1998; 120(12): 743-749.-   11. Andriacchi T P, Lang P, Alexander E, Hurwitz D: Methods for    evaluating the progression of osteoarthritis. J Rehab Res Develop    2000; 37, 2: 163-170.-   12. Andriacchi T P, Sen K, Toney M K, Yoder D: New developments in    musculoskeletal testing. Proceedings of the Canadian Society of    Biomechanics, 1994.-   13. Andriacchi T P, Strickland A B: Gait analysis as a tool to    assess joint kinetics biomechanics of normal and pathological human    articulating joints. Nijhoff, Series E 1985; 93: 83-102.-   14. Andriacchi T P, Toney M K: In vivo measurement of    six-degrees-of-freedom knee movement during functional testing.    Transactions of the Orthopedic Research Society 1995: 698.-   15. Beaulieu C F, Hodge D K, Bergman A G: Glenohumeral relationships    during physiological shoulder motion and stress testing: initial    experience with open MRI and active scan-plane registration.    Radiology 1999: accepted for publication.-   16. Beaulieu C F, Hodge D K, Thabit G, Lang P K, Bergman A G:    Dynamic imaging of glenohumeral instability with open MRI. Int.    Society for Magnetic Resonance in Medicine, Sydney, Australia, 1998.-   17. Benedetti M G, Cappozzo A: Anatomical landmark definition and    identification in computer aided movement analysis in a    rehabilitation context II (Internal Report). U Degli Studi La    Sapienza 1994: 1-31.-   18. Bergman A G, Beaulieu C F, Pearle A D, et al.: Joint motion:    assessment by upright interactive dynamic near-real time MR imaging.    Radiological Society of North America, 83rd Scientific Assembly and    Annual Meeting, Chicago, Ill., 1997.-   19. Biswal S, Hastie T, Andriacchi T, Bergman G, Dillingham M F,    Lang P: The rate of progressive cartilage loss at the knee is    dependent on the location of the lesion: a longitudinal MRI study in    43 patients. Arthritis & Rheumatism 2000: submitted for publication.-   20. Bobic V: Arthoscopic osteochondral autograft transplantation in    anterior cruciate ligament reconstruction: a preliminary clinical    study. Knee Surg Sports Traumatol Arthrosc 1996; 3 (4): 262-264.-   21. Boe S, Hansen H: Arthroscopic partial meniscectomy in patients    aged over 50. J Bone Joint Surg 1986; 68B: 707.-   22. Bregler. C, Hertzmann A, Biermann H: Recovering non-rigid 3D    shape from image streams. Proc. IEEE Conference on Computer Vision    and Pattern Recognition 2000: in press.-   23. Brittberg M, Lindahl A, Homminga G, Nilsson A, Isaksson O,    Peterson L: A critical analysis of cartilage repair. Acta Orthop    Scand 1997; 68 (2) 186-191.-   24. Brittberg M, Lindahl A, Nilsson A, Ohlsson C, Isaksson O,    Peterson L: Treatment of deep cartilage defects in the knee with    autologous chondrocyte transplantation. N Engl J Med 1994; 331 (14):    889-895.-   25. Broderick L S, Turner D A, Renfrew D L, Schnitzer T J, Huff J P,    Harris C: Severity of articular cartilage abnormality in patients    with osteoarthritis: evaluation with fast spin-echo MR vs    arthroscopy. AJR 1994; 162: 99-103.-   26. Butts K, Pauly J M, Kerr A B, Bergman A G, Beaulieu C F:    Real-Time MR imaging of joint motion on an open MR imaging scanner.    Radiological Society of North America, 83rd Scientific Assembly and    Annual Meeting, Chicago, Ill., 1997.-   27. Cohen Z A, McCarthy D M, Kwak, S D, Legrand P, Fogarasi F,    Ciaccio E J, Ateshian G A: Knee cartilage topography, thickness, and    contact areas from MRI: in-vitro calibration and in-vivo    measurements. Osteoarthritis and Cartilage 1999; 7: 95-109.-   28. Daniel B, Butts K, Glover G, Herfkens R: Breast cancer:    gadolinium-enhanced MR imaging with a 0.5 T open imager and    three-point Dixon technique. Radiology 1998; 207 (1): 183-190.-   29. Disler D G: Fat-suppressed three-dimensional spoiled    gradient-recalled MR imaging: assessment of articular and physeal    hyaline cartilage. AJR 1997; 169: 1117-1123.-   30. Disler D G, McCauley T R, Kelman C G, et al.: Fat-suppressed    three-dimensional spoiled gradient-echo MR imaging of hyaline    cartilage defects in the knee: comparison with standard MR imaging    and arthroscopy. AJR 1996; 167: 127-132.-   31. Disler D G, McCauley T R, Wirth C R, Fuchs M D: Detection of    knee hyaline cartilage defects using fat-suppressed    three-dimensnional spoiled gradient-echo MR imaging: comparison with    standard MR imaging and correlation with arthrosocpy. AJR 1995; 165:    377-382.-   32. Doherty M, Hutton C, Bayliss M T: Osteoarthritis. In: Maddison P    J, Isenberg D A, Woo P, et al., eds. Oxford Textbook of    Rheumatology, vol 1. Oxford, N.Y., Tokyo: Oxford University Press,    1993; 959-983.-   33. Dougados M, Gueguen A, Nguyen M, et al.: Longitudinal radiologic    evaluation of osteoarthritis of the knee. J Rheumatol 1992; 19:    378-384.-   34. Du Y P, Parker D L, Davis W L: Vessel enhancement filtering in    three-dimensional MR angiography. J Magn Res Imaging 1995; 5:    151-157.-   35. Du Y P, Parker D L, Davis W L, Cao G: Reduction of    partial-volume artifacts with zero-filled interpolation in    three-dimensional MR angiography. J Magn Res Imaging 1994; 4:    733-741.-   36. Dumoulin C L, Souza S P, Darrow R D: Real-time position    monitoring of invasive devices using magnetic resonance. Magn Reson    Med 1993; 29: 411-5.-   37. Dyrby C O: The three-dimensional kinematics of knee joint    motion: functional differences in two populations [Master's Thesis].    University of Illinois at Chicago; 1998.-   38. Eckstein F, Westhoff J, Sittek H, et al.: In vivo    reproducibility of three-dimensional cartilage volume and thickness    measurements with MR imaging. AJR 1998; 170(3): 593-597.-   39. Elting J J, Hubbell J C: Unilateral frame distraction: proximal    tibial valgus osteotomy for medial gonarthritis. Contemp Orthop    1993; 27(6): 522-524.-   40. Falcao A X, Udupa J K, Samarasekera S, Sharma S: User-steered    image segmentation paradigms: Live wire and live lane. Graphical    Models and Image Processing 1998; 60: 233-260.-   41. Felson D T, Zhang Y, Anthony J M, Naimark A, Anderson J J:    Weight loss reduces the risk for symptomatic knee osteoarthritis in    women: the Framingham study. Ann Intern Med 1992; 116: 535-539.-   42. Garrett J C: Osteochondral allografts for reconstruction of    articular defects of the knee. Instr Course Lect 1998; 47: 517-522.-   43. Ghosh S, Newitt D C, Majumdar S: Watershed segmentation of high    resolution articular cartilage image. International Society for    Magnetic Resonance in Medicine, Philadelphia, 1999.-   44. Gouraud H: Continuous shading of curved surfaces. IEEE Trans on    Computers 1971; C-20(6).-   45. Gray A: Modern Differential Geometry of Curves and Surfaces.    1993: CRC Press, Inc.-   46. Hargreaves B A, Gold G E, Conolly S M, Nishimura D G: Technical    considerations for DEFT imaging. International Society for Magnetic    Resonance in Medicine, Sydney, Australia, Apr. 17-24, 1998.-   47. Hargreaves B A, Gold G E, Lang P K, Bergman G, Conolly S M,    Nishimura D G: Imaging of articular cartilage using driven    equilibrium. International Society for Magnetic Resonane in    Medicine, Sydney, Australia, Apr. 17-24, 1998.-   48. Hayes C, Conway W: Evaluation of articular cartilage:    radiographic and cross-sectional imaging techniques. Radiographics    1992; 12: 409428.-   49. Henkelman R M, Stanisz G, Kim J, Bronskill M: Anisotropy of NMR    properties of tissues. Magn Res Med 1994; 32: 592-601.-   50. Hoppenfeld S, Huton R: Physical Examination of the Knee. In:    Hoppenfeld S, ed. Physical Examination of the Spine and Extremities:    Appleton-Century-Crofts/Prentice-Hall, 1976; 171-196.-   51. Hyhlik-Durr A, Faber S, Burgkart R, et al.: Precision of tibial    cartilage morphometry with a coronal water-excitation MR sequence.    European Radiology 2000; 10 (2): 297-303.-   52. Irarrazabal P, Nishimura D G: Fast three-dimensional magnetic    resonance imaging. Mag Res Med 1995; 33: 656-662.-   53. Johnson F, Leitl S, Waugh W: The distribution of load across the    knee. A comparison of static and dynamic measurements. J Bone Joint    Surg 1980; 62B: 346-349.-   54. Johnson T S: In vivo contact kinematics of the knee joint:    Advancing the point cluster technique. Ph.D. thesis, University of    Minnesota 1999.-   55. Johnson T S, Andriacchi T P, Laurent M: Development of a knee    wear method based on prosthetic in vivo slip velocity. Transactions    of the Orthopedic Research Society, 46th Annual Meeting, March,    2000.-   56. LaFortune M A, Cavanagh P R, Sommer H J, Kalenak A: Three    dimensional kinematics of the human knee during walking. J.    Biomechanics 1992; 25: 347-357.-   57. Lang P, Alexander E, Andriacchi T: Funcional joint imaging: a    new technique integrating MRI and biomotion studies. International    Society for Magnetic Resonance in Medicine, Denver, Apr. 18,    2000-Apr. 24, 2000, 2000.-   58. Lang P, Biswal S, Dillingham M, Bergman G, Hastie T, Andriacchi    T: Risk factors for progression of cartilage loss: a longitudinal    MRI study. European Society of Musculoskeletal Radiology, 6th Annual    Meeting, Edinburgh, Scotland, 1999.-   59. Lang P, Hargreaves B A, Gold G, et al.: Cartilage imaging:    comparison of driven equilibrium with gradient-echo, SPGR, and fast    spin-echo sequences. International Society for Magnetic Resonance in    Medicine, Sydney, Australia, Apr. 17-24, 1998.-   60. Ledingham J, Regan M, Jones A, Doherty. M: Factors affecting    radiographic progression of knee osteoarthritis. Ann Rheum Dis.    1995; 54: 53-58.-   61. Lorensen W E, Cline H E: Marching cubes: a high resolution 3d    surface construction algorithm. Comput Graph 1987; 21: 163-169.-   62. Losch A, Eckstein F, Haubner M, Englmeier K H: A non-invasive    technique for 3-dimensional assessment of articular cartilage    thickness based on MRI part 1: development of a computational    method. Magn Res Imaging 1997; 15, 7: 795-804.-   63. Lu T W, O'Connor J J: Bone position estimation from skin marker    co-ordinates using globals optimisation with joint constraints. J    Biomechanics 1999; 32: 129-134.-   64. Lucchetti L, Cappozzo A, Cappello A, Della Croce U: Skin    movement artefact assessment and compensation in the estimation of    knee-joint kinematics. J Biomechanics 1998; 31: 977-984.-   65. Lynch J A, Zaim S, Zhao J, Stork A, Genant H K: Cartilage    segmentation of 3D MRI scans of the osteoarritic knee combining user    knowledge and active contours. Proc. SPIE 3979 Medical Imaging, San    Diego, February 2000.-   66. Maki J H, Johnson G A, Cofer G P, MacFall J R: SNR improvement    in NMR microscopy using DEFT. J Mag Res 1988.-   67. Meyer C H, Pauly J M, Macovski A, Nishimura D G: Simultaneous    spatial and spectral selective excitation. Magn Res Med 1990; 15:    287-304.-   68. Mollica Q, Leonardi W, Longo G, Travaglianti G: Surgical    treatment of arthritic varus knee by tibial corticotomy and angular    distraction with an external fixator. Ital J Orthop Traumatol 1992;    18 (1): 17-23.-   69. Nizard R S: Role of tibial osteotomy in the treatment of medial    femorotibial osteoarthritis. Rev Rhum Engl Ed 1998; 65 (7-9):    443-446.-   70. Noll D C, Nishimura D, Macovski A: Homodyne detection in    magnetic resonance imaging. IEEE Trans Med Imag 10 1991; 10 (2):    154-163.-   71. Ogilvie-Harris D J, Fitsialos D P: Arthroscopic management of    the degenerative knee. Arthroscopy 1991; 7: 151-157.-   72. Pearle A, Bergman A G, Daniels B, et al.: Use of an external    MR-tracking coil for active scan plane registration during dynamic    musculoskeletal MR imaging in a vertically open MRT unit. American    Roentgen Ray Society, San Francisco, Calif., 1998.-   73. Pearle A D, Daniel B L, Bergman A G: Joint motion in an open MR    unit using MR tracking. JMRI 1999; 10 (10): 1566-1576.-   74. Peterfy C, van Dijke C, Lu Y, et al.: Quantification of the    volume of articular cartilage in the metacarpophalangeal joints of    the hand: accuracy and precision of three-dimensional MR imaging.    AJR 1995; 165: 371-375.-   75. Peterfy C G, Majumdar S, Lang P, van Dijke C, Sack K, Genant H    K: MR imaging of the arthritic knee: improved discrimination of    cartilage, synovium, and effusion with pulsed saturation transfer    and fat-suppressed T1-weighted sequences. Radiology 1994; 191(2):    413-419.-   76. Peterfy C G, van Dijke C F, Janzen D L, et al.: Quantification    of articular cartilage in the knee with pulsed saturation transfer    subtraction and fat-suppressed MR imaging: optimization and    validation. Radiology 1994; 192(2): 485-491.-   77. Piplani M A, Disler D G, McCauley T R, Holmes T J, Cousins J P:    Articular cartilage volume in the knee: semiautomated determination    from three-dimensional reformations of MR images. Radiology 1996;    198: 855-859.-   78. Potter H G, Linklater J M, Allen A A, Hannafm J A, Haas S B:    Magnetic resonance imaging of articular cartilage in the knee: an    evaluation with use of fast-spin-echo imaging. J Bone Joint Surg    1998; 80-A(9): 1276-1284.-   79. Prodromos C C, Andriacchi T P, Galante J O: A relationship    between gait and clinical changes following high tibial osteotomy. J    Bone Joint Surg 1985; 67A: 1188-1194.-   80. Radin E L, Burr D B, Caterson B, Fyhrie D, Brown T D, Boyd R D:    Mechanical determinants of osteoarthrosis. Sem Arthr Rheum 1991;    21(3): 12-21.-   81. Radin E L, Burr D B, Fyhrie D: Characteristics of joint loading    as it applies to osteoarthrosis. In: Mow V C, Woo S-Y, Ratcliffe T,    eds. Symposium on Biomechanics of Diarthrodial Joints, vol 2. New    York, N.Y.: Springer-Verlag, 1990; 437-451.-   82. Recht M P, Piraino D W, Paletta G A, Schils J P, Belhobek G H:    Accuracy of fat-suppressed three-dimensional spoiled gradient-echo    FLASH MR imaging in the detection of patellofemoral articular    cartilage abnormalities. Radiology 1996; 198: 209-212.-   83. Recht M P, Resnick D: MR imaging of articular cartilage: current    status and future directions. AJR 1994; 163: 283-290.-   84. Ritter M A, Faris P M, Keating E M, Meding J B: Postoperative    alignment of total knee replacement. Clin Orthop 1994; 299: 153-156.-   85. Saito T, Toriwaki J-I: New algorithms for Euclidean distance    transformation of an n-dimensional digitized picture with    applications. Pattern Recognition 1994; 27 (11): 1551-1565.-   86. Schipplein O D, Andriacchi T P: Interaction between active and    passive knee stabilizers during level walking. J Orthop Res 1991; 9:    113-119.-   87. Schouten J S A G, van den Ouweland F A, Valkenburg H A: A 12    year follow up study in the general population on prognostic factors    of cartilage loss in osteoarthritis of the knee. Ann Rheum Dis 1992;    51: 932-937.-   88. Sharif M, George E, Shepstone L, et al.: Serum hyaluronic acid    level as a predictor of disease progression in osteoarhritis of the    knee. Arthritis Rheum 1995; 38: 760-767.-   89. Sharma L, D. E. H, Thonar E J M A, et al.: Knee adduction    moment, serum hyaluronic acid level, and disease severity in medial    tibiofemoral osteoarthritis. Arthritis and Rheumatism 1998; 41(7):    1233-40.-   90. Shoup R R, Becker E D: The driven equilibrium Fourier transform    NMR technique: an experimental study. J Mag Res 1972; 8.-   91. Slemenda C, Mazzuca S, Brandt K, Katz B: Lower extremity lean    tissue mass and strength predict increases in pain and in functional    impairment in knee osteoarthritis. Arthritis Rheum 1996; 39(suppl):    S212.-   92. Slemenda C, Mazzuca S, Brandt K, Katz B: Lower extremity    strength, lean tissue mass and bone density in progression of knee    osteoarthritis. Arthritis Rheum 1996; 39(suppl): S169.-   93. Solloway S, Hutchinson C E, Waterton J C, Taylor C J: The use of    active shape models for making thickness measurements of articular    cartilage from MR images. Mag Res Med 1997; 37:943-952.-   94. Spoor C W, Veldpas F E: Rigid body motion calculated from    spatial coordinates of markers. J Biomechanics 1980; 13: 391-393.-   95. Stamrnmberger T, Eckstein F, Englmeier K H, Reiser M:    Determination of 3D cartilage thickness data from MR imaging:    computational method and reproducibility in the living. Mag Res Med    1999; 41: 529-536.-   96. Stammberger T, Eckstein F, Michaelis M, Englmeier K H, Reiser M:    Interobserver reproducibility of quantitative cartilage    measurements: Comparison of B-spline snakes and manual segmentation.    Mag Res Imaging 1999; 17:1033-1042.-   97. Steines D, Berger F, Cheng C, Napel S, Lang P: 3D thickness maps    of articular cartilage for quantitative assessment of    osteoarthritis. To be presented at ACR 64th Annual Scientific    Meeting, Philadelphia, October 2000.-   98. Steines D, Cheng C, Wong A, Berger F, Napel S, Lang P:    Segmentation of osteoarthritic femoral cartilage from MR images.    CARS—Computer-Assisted Radiology and Surgery, p. 578-583, San    Francisco, 2000.-   99. Steines D, Napel S, Lang P: Measuring volume of articular    cartilage defects in osteoarthritis using MRI. To be presented at    ACR 64th Annual Scientific Meeting, Philadelphia, October 2000.-   100. Stevenson S, Dannucci G A, Sharkey N A, Pool R R: The fate of    articular cartilage after transplantation of fresh and cryopreserved    tissue-antigen-matched and mismatched osteochondral allografts in    dogs. J Bone Joint Surg 1989; 71 (9): 1297-1307.-   101. Tieschky M, Faber S, Haubner M, et al.: Repeatability of    patellar. cartilage thickness patterns in the living, using a    fat-suppressed magnetic resonance imaging sequence with short    acquisition time and three-dimensional data processing. J Orthop Res    1997; 15(6): 808-813.-   102. Tomasi C, Kanade T: Shape and motion from image streams under    orthography—a factorization method. Proc Nat Acad Sci 1993; 90(21):    9795-9802.-   103. Tsai J, Ashjaee S, Adalsteinsson E, et al.: Application of a    flexible loop-gap resonator for MR imaging of articular cartilage at    3.0 T. International Society for Magnetic Resonance in Medicine,    Denver, Apr. 18, 2000-Apr. 24, 2000, 2000.-   104. Wang J W, Kuo K N, Andriacchi T P, Galante J O: The influence    of walking mechanics and time on the results of proximal tibial    osteotomy. J Bone Joint Surg 1990; 72A: 905-909.-   105. Waterton J C, Solloway S, Foster J E, Keen M C, Gandy S,    Middleton B J, Maciewicz R A, Watt I, Dieppe P A, Taylor C J:    Diurnal variation in the femoral articular cartilage of the knee in    young adult humans. Mag Res Med 2000, 43: 126-132.-   106. Woolf S D, Chesnick F, Frank J, Lim K, Balaban R: Magnetization    transfer contrast: MR imaging of the knee. Radiology 1991; 179:    623-628.-   107. Worring M, Smeulders A W M: Digital curvature estimation.    CVGIP: Image Understanding, 1993. 58(3): p. 366-382.-   108. Yan C H: Measuring changes in local volumetric bone density:    new approaches to quantitative computed tomography, Ph.D. thesis,    1998, Dept. of Electrical Engineering, Stanford University-   109. Yao L, Gentili A, Thomas A: Incidental magnetization transfer    contrast in fast spin-echo imaging of cartilage. J Magn Reson    Imaging 1996; 6 (1): 180-184.-   110. Yao L, Sinha S, Seeger L: MR imaging of joints: analytic    optimization of GRE techniques at 1.5 T. AJR 1992; 158(2): 339-345.-   111. Yasuda K, T. M, Tsuchida T, Kameda K: A 10 to 15 year follow up    observation of high tibial osteotomy in medial compartment    osteoarthritis. Clin Orthop 1992; 282: 186-195.-   112. Kass M, Witkin A, Terzopoulos D: Snakes: Active contour models.    Int J Comput Vision 1988; 1:321-331-   113. Falcao A X, Udupa J K, Samarasekera S, Sharma S, Hirsch B E,    Lotufo R A: User-steered image segmentation paradigms: Live wire and    live lane. GMIP 1998; 60, 233-260-   114. Steines, D., et al., Segmentation of osteoarthritic femoral    cartilage using live wire, ISMRM Eight Scientific Meeting, Denver    Colo., 2000

SUMMARY OF THE INVENTION

This invention relates to assessing the condition of a joint of amammal, particularly a human subject, using the assessment to treat andmonitor the subject as needed for cartilage degeneration problems. Whilethe numerous aspects of the invention are useful for joints generally,they are particularly suited for dealing with the human knee. Someaspects related the static images and degeneration patterns of acartilage, while others relate to the interaction of such images andpatterns to provide a better means of assessing the condition of acartilage.

One aspect of this invention is a method for assessing the condition ofa cartilage. The method comprises obtaining an image of a cartilage,(preferably a magnetic resonance image), converting the image to athree-dimensional degeneration pattern, and evaluating the degree ofdegeneration in a volume of interest of the cartilage. By performingthis method at an initial time T, and a later time T₂, one can determinethe change in the volume of interest and evaluate what steps to take fortreatment.

Another aspect of this invention is a method of estimating the loss ofcartilage in a joint. The method comprises obtaining a three-dimensionalmap of the cartilage at an initial time and calculating the thickness orregional volume of a region thought to contain degenerated cartilage somapped at the initial time, obtaining a three-dimensional map of thecartilage at a later time, and calculating the thickness or regionalvolume of the region thought to contain degenerated cartilage so mappedat the later time, and determining the loss in thickness or regionalvolume of the cartilage between the later and initial times. The 3D mapmay be a thickness map, a biochemical map or a combination.

Another aspect of the invention is a method for assessing the conditionof cartilage in a joint of a human, which method compriseselectronically transferring an electronically-generated image of acartilage of the joint from a transferring device to a receiving devicelocated distant from the transferring device; receiving the transferredimage at the distant location; converting the transferred image to adegeneration pattern of the cartilage; and transmitting the degenerationpattern to a site for analysis.

Another aspect of the invention is a method for determining the volumeof cartilage loss in a region of a cartilage defect of a cartilage injoint of a mammal. The method comprises (a) determining the thickness,D_(N), of the normal cartilage near the cartilage defect; (b) obtainingthe thickness of the cartilage defect, D_(D), of the region; (c)subtracting D_(D) from D_(N) to give the thickness of the cartilageloss, D_(L); and (d) multiplying the D_(L) value times the area of thecartilage defect, A_(D), to give the volume of cartilage loss.

Still another aspect of the invention is a method of estimating thechange of a region of cartilage in a joint of a mammal over time. Themethod comprises (a) estimating the width or area or volume of a regionof cartilage at an initial time T₁, (b) estimating the width or area orvolume of the region of cartilage at a later time T₂, and (c)determining the change in the width or area or volume of the region ofcartilage between the initial and the later times.

Still another aspect of the invention is a method of estimating the lossof cartilage in a joint. The method comprises (a) defining a 3D objectcoordinate system of the joint at an initial time, T₁; (b) identifying aregion of a cartilage defect within the 3D object coordinate system; (c)defining a volume of interest around the region of the cartilage defectwhereby the volume of interest is larger than the region of cartilagedefect, but does not encompass the entire articular cartilage; (d)defining the 3D object coordinate system of the joint at a secondtimepoint, T₂; (e) placing the identically-sized volume of interest intothe 3D object coordinate system at timepoint T₂ using the objectcoordinates of the volume of interest at timepoint T₁; (f) and measuringany differences in cartilage volume within the volume of interestbetween timepoints T₁, and T₂.

Another aspect of this invention is a method for providing abiochemically-based map of joint cartilage. The method comprisesmeasuring a detectable biochemical component throughout the cartilage,determining the relative amounts of the biochemical component throughoutthe cartilage; mapping the amounts of the biochemical component throughthe cartilage; and determining the areas of cartilage deficit byidentifying the areas having an altered amount of the biochemicalcomponent present.

Once a map is obtained, it can be used in assessing the condition of acartilage at an initial time and over a time period. Thus, thebiochemical map may be used in the method aspects of the invention in amanner similar to the cartilage thickness map.

Another aspect of this invention is a method for assessing the conditionof cartilage in a joint from a distant location. The method compriseselectronically transferring an electronically-generated image of acartilage of the joint from a transferring device to a receiving devicelocated distant from the transferring device; receiving the transferredimage at the distant location; converting the transferred image to adegeneration pattern of the cartilage; and transmitting the degenerationpattern to a site for analysis.

Another aspect of the invention is a kit for aiding in assessing thecondition of cartilage in a joint of a mammal, which kit comprises asoftware program, which when installed and executed on a computer readsa cartilage degeneration pattern presented in a standard graphics formatand produces a computer readout showing a cartilage thickness map of thedegenerated cartilage.

Another aspect of this invention is a method for assessing the conditionof a subject's cartilage in a joint, the method comprises obtaining athree dimensional biochemical representation of the cartilage, obtaininga morphological representation of the cartilage, and merging the tworepresentations, and simultaneously displaying the mergedrepresentations on a medium. The merged representations are then used toassess the condition of a cartilage, estimate the loss of cartilage in ajoint, determining the volume of cartilage loss in a region of cartilagedefect, or estimating the change of a region of cartilage at aparticular point in time or over a period of time.

A method for correlating cartilage image data, bone image data, andopto-electrical image data for the assessment of the condition of ajoint, which method comprises (a) obtaining the bone image data of thejoint with a set of skin reference markers positioned in externally nearthe joint, (b) obtaining the opto-electrical image data of the jointwith a set of skin reference markers positioned in the same manner as(a), and (c) using the skin reference markers to correlate the imagesobtained in (a) and (b) with each other, wherein each skin referencemarker is detectable in the bone data and the opto-electrical data. Themethod also can be used to further evaluate cartilage image data that isobtained using a similarly positioned set of skin reference markers.

Another aspect of the invention is a skin reference marker thatcomprises (a) a material detectable by an imaging technique; (b) acontainer for holding the material, (c) a material that causes thecontainer to adhere to the skin of a human, and (d) a reflectivematerial placed on the surface of the container.

Another aspect of the invention is a biochemical map of a cartilage thatcomprises a three-dimensional representation of the distribution of theamount of the biochemical component throughout the cartilage.

Another aspect of the invention is a method for providing abiochemically-based map of joint cartilage of a mammal, wherein thejoint comprises cartilage and associated bones on either side of thejoint, which method comprises (a) measuring a detectable biochemicalcomponent throughout the cartilage; (b) determining the relative amountsof the biochemical component throughout the cartilage; (c) mapping theamounts of the biochemical component in three dimensions through thecartilage; and (d) determining the areas of abnormal joint cartilage byidentifying the areas having altered amounts of the biochemicalcomponent present.

Another aspect of the invention is a method for deriving the motion ofbones about a joint from markers placed on the skin, which methodcomprises (a) placing at least three external markers on the patient'slimb segments surrounding the joint, (b) registering the location ofeach marker on the patient's limb while the patient is standingcompletely still and while moving the limb, (c) calculating theprincipal axis, principal moments and deformation of rigidity of thecluster of markers, and (d) calculating a correction to the artifactinduced by the motion of the skin markers relative to the underlyingbone.

Another aspect of the invention is a system for assessing the conditionof cartilage in a joint of a human, which system comprises (a) a devicefor electronically transferring a cartilage degeneration pattern for thejoint to a receiving device located distant from the transferringdevice; (b) a device for receiving the cartilage degeneration pattern atthe remote location; (c) a database accessible at the remote locationfor generating a movement pattern for the joint of the human wherein thedatabase includes a collection of movement patterns of human joints,which patterns are organized and can be accessed by reference tocharacteristics such as type of joint, gender, age, height, weight, bonesize, type of movement, and distance of movement; (d) a device forgenerating a movement pattern that most closely approximates a movementpattern for the human patient based on the characteristics of the humanpatient; (e) a device for correlating the movement pattern with thecartilage degeneration pattern; and (f) a device for transmitting thecorrelated movement pattern with the cartilage degeneration pattern backto the source of the cartilage degeneration pattern.

A method for assessing the condition of the knee joint of a humanpatient, wherein the knee joint comprises cartilage and associated boneson either side of the joint, which method comprises (a) obtaining thepatient's magnetic resonance imaging (MRI) data of the knee showing atleast the bones on either side of the joint, (b) segmenting the MRI datafrom step (a), (c) generating a geometrical representation of the boneof the joint from the segmented MRI data, (d) assessing the patient'sgait to determine the load pattern or the cartilage contact pattern ofthe articular cartilage in the joint during the gait assessment, and (e)correlating the load pattern or cartilage contact pattern obtained instep (d) with the geometrical representation obtained in step (c).

Another aspect of the invention is a method of assessing the rate ofdegeneration of cartilage in the joint of a mammal, wherein the jointcomprises cartilage and the bones on either side of the cartilage, whichmethod comprises. (a) obtaining a cartilage degeneration pattern of thejoint that shows an area of greater than normal degeneration, (b)obtaining a movement pattern of the joint that shows where the opposingcartilage surfaces contact, (c) comparing the cartilage degenerationpattern with the movement pattern of the joint, and (d) determining ifthe movement pattern shows contact of one cartilage surface with aportion of the opposing cartilage surface showing greater than normaldegeneration in the cartilage degeneration pattern.

Another aspect of the invention is a method for monitoring the treatmentof a degenerative joint condition in a mammal, wherein the jointcomprises cartilage and accompanying bones on either side of the joint,which method comprises (a) comparing the movement pattern of the jointwith the cartilage degeneration pattern of the joint; (b) determiningthe relationship between the movement pattern and the cartilagedegeneration pattern; (c) treating the mammal to minimize furtherdegeneration of the joint condition; and (d) monitoring the treatment tothe mammal.

Still another aspect of the invention is a method of assessing thecondition of a joint in a mammal, wherein the joint comprises cartilageand accompanying bones on either side of the joint, which methodcomprises (a) comparing the movement pattern of the joint with thecartilage degeneration pattern of the joint; and (b) determining therelationship between the movement pattern and the cartilage degenerationpattern.

Another aspect of the invention is a method of assessing a joint diseasethat includes obtaining an electronic image of a joint. The image iselectronically evaluated to obtain at least one of volume, area,thickness, curvature, geometry, shape, biochemical contents, signalintensity and relaxation time of normal and/or diseased tissue. One ormore axes related to the joint is determined.

In related embodiments of the invention, the axis determined may be ananatomic axis and/or a biomechanical axis. Obtaining the electronicimage may include performing an MRI, a CT, a spiral CT, an ultrasoundand/or an x-ray. Obtaining the electronic image may include using acontrast agent. At least one of a cast, physical model and/or mold maybe created of all or portions of said joint. The method may furtherinclude guiding a treatment as a function of said electronicallyevaluating and said determining an axis. The guiding may includeoptimizing the alignment of a treatment, such as optimizingbiomechanical forces applied to the joint.

Another aspect of the invention is a method of assessing a jointdisease. The method includes obtaining an electronic image of a joint.The image is electronically evaluated to obtain at least one of volume,area, thickness, curvature, geometry, shape, biochemical contents,signal intensity and relaxation time of normal and/or diseased tissue.At least one of a cast, a physical model, and a mold is created of allor a portion of the joint. One or more movement patterns of the joint isevaluated.

In related embodiments of the invention, evaluation of the movementpattern includes testing the joint in at least one or more flexionangles and/or extension angles, at least one or more abduction angles,at least one or more adduction angles and/or at least one or morerotation angles.

Yet another aspect of the invention is a method of assessing a jointdisease that includes obtaining an electronic image of a joint. Theimage is electronically evaluated to obtain at least one of volume,area, thickness, curvature, geometry, shape, biochemical contents,signal intensity and relaxation time of normal and/or diseasedcartilage. A cast, physical model, and/or a mold is created of all or aportion of said joint.

Still another aspect of the invention is a method of assessing a jointdisease that includes obtaining an electronic image of a joint. Theimage is electronically evaluated to obtain at least one of volume,area, thickness, curvature, geometry, shape, biochemical contents,signal intensity and relaxation time of normal and/or diseased cartilageand subchondral bone. A cast, a physical model, and/or a mold is createdof all or a portion of said joint.

Another aspect of the invention is a method of assessing a joint diseasethat includes obtaining an electronic image of a joint. The image iselectronically evaluated to obtain at least one of volume, area,thickness, curvature, geometry, shape, biochemical contents, signalintensity or relaxation time of the joint. Cartilage or subchondral boneassociated with the joint is segmented. A cast, physical model, and/ormold of all or a portion of said joint is created.

In related embodiments, the segmention includes using at least one of anactive contour algorithm, a livewire algorithm, thresholding, a seedgrowing algorithm, a texture based algorithm and a model basedsegmentation algorithm.

In accordance with another aspect of the invention, an articular repairsystem to treat a mammalian with joint disease including arthritis ispresented. The articular repair system has a shape that substantiallyfollows the physical shape of the joint.

In related embodiments of the invention, the physical shape of the jointis determined by the shape of articular cartilage, the subchondral bone,and/or the shape of articular cartilage and subchondral bone. Thearticular repair system may be metallic. The articular repair system mayreplace all or portions of one or more articular surfaces. The articularrepair system may substantially conform to a tibia, a femur, anacetabulum, or a femoral head. The physical shape may be one or moreportions of a tibia or femur, including one or more underlyingconvexities or concavities.

In accordance with another aspect of the invention, an articular repairsystem to treat a mammalian with joint disease including arthritis ispresented. The articular repair system has a thickness thatsubstantially matches the thickness of the articular cartilage.

In accordance with still another aspect of the invention, a method ofassessing cartilage disease or damage in a joint of a living subject ispresented. The joint includes cartilage and/or accompanying bone oneither side of the joint. The method includes (a) obtaining athree-dimensional volumetric representation of cartilage of said jointdemonstrating volume or thickness or biochemical contents or relaxationtime of both normal and diseased or damaged cartilage of said joint, and(b) electronically estimating thickness or area or volume of lostcartilage tissue relative to expected cartilage tissue in absence ofdisease or damage. The representation is used to devise a treatment fordamaged or diseased cartilage and/or bone.

In related embodiments of the invention, the thickness, area or volumeof said cartilage tissue that has been lost is obtained by determining amargin between said diseased or damaged cartilage and the normalcartilage in the three-dimensional map. The margin may be determined bymeasuring changes in the thickness of said normal and said diseasedcartilage, the biochemical content of said normal and said diseasedcartilage, and/or in the relaxation time of the normal and the diseasedcartilage. The method may be carried out at an initial time T₁ and at alater time T₂ and said determination includes a determination of amountof cartilage lost between T₁ and T₂. The amount of cartilage tissue lostmay be determined as thickness or area or volume or content in one ormore biochemical components of said cartilage tissue lost. Thethickness, area, volume, or content of said cartilage tissue lost may beobtained by determining a margin between diseased or damaged cartilageand normal cartilage. The change in the diseased cartilage or thecartilage tissue lost may be determined without matching data obtainedat T₁ and T₂.

Yet another aspect of the invention is a method of assessing cartilagedisease or damage in a joint of a living subject is presented. The jointincludes cartilage and/or bone on either side of the joint. The methodincludes obtaining a three-dimensional volumetric representation ofcartilage of the joint. The volumetric representation includesinformation on volume, thickness, curvature, shape and/or relaxationtime of both normal and damaged cartilage of said joint. A volume ofinterest smaller than the articular cartilage surface is electronicallyplaced in or around an area of diseased or damaged cartilage. Therepresentation is used to devise a treatment for damaged or diseasedcartilage or bone.

Still another aspect of the invention is a method of assessing cartilagedisease or damage in a joint of a living subject. The joint includescartilage and/or bone on either side of the joint. The method includesobtaining a three-dimensional dataset of the articular cartilage thatincludes information on volume, thickness, biochemical contents and/orrelaxation time of said cartilage. A subregion that is smaller than theentire articular surface is electronically evaluated. A quantitativemeasurement is performed of volume, thickness, biochemical contentsand/or relaxation time of the cartilage in the subregion. Therepresentation is used to devise a treatment for damaged or diseasedcartilage or bone.

Another aspect of the invention is a method of assessing articulardamage in a joint of a living subject. The joint includes cartilageand/or bone on either side of the joint. The method includes obtaining athree-dimensional dataset of the joint that includes information onvolume, thickness, shape, biochemical contents and/or relaxation time ofthe cartilage and/or bone. The cartilage and/or said bone is segmented.The segmentation is used to devise a treatment for damaged or diseasedcartilage or bone. The method may further include transferring thedataset to a remote site.

Still another aspect of the invention is a method of assessing articulardamage in a joint of a living subject. The joint includes cartilageand/or bone on either side of the joint. The method includes obtaining athree-dimensional dataset of the joint that includes information onvolume, thickness, shape, biochemical contents and/or relaxation time ofsaid cartilage or bone. A normal or near normal outer cartilage surfaceis reconstructed over an area of cartilage loss or diseased cartilage.

In accordance with related embodiments of the invention, thereconstructed outer cartilage surface may be used to guide, devise orselect a treatment for damaged or diseased cartilage or bone. Thedataset may be transferred to a remote site.

Other aspects of the invention may be apparent upon further reading thespecification and claims of the patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 shows an overview schematic representation of some aspects of theinvention of this application.

FIG. 2 shows a DEFT pulse sequence.

FIG. 3A shows the signal levels for cartilage and synovial fluid withRARE (FIG. 3A) and DEFT (FIG. 3B) pulse sequences, both TE=14milliseconds.

FIG. 4 shows the mean contrast to noise ratio (CNR) of cartilage tojoint fluid for various MRI pulse sequences.

FIG. 5 shows the mean contrast for cartilage and joint fluid for variousMRI pulse sequences.

FIG. 6 shows a DEFT acquisition using non-selective refocusing pulses tomaximize the SNR efficiency and a partial K-Echo-Plainer acquisitiongradients in order to minimize the required scan time for 3D volume.

FIG. 7 shows four sample images acquired with a DEFT pulse sequencecombined with a partial K-Echo-Plainer acquisition in order to provideefficient 3D coverage.

FIGS. 8A and 8B show a 3-point Dixon GRE image of the articularcartilage of medial fermorotibial compartment in a normal 35-year oldvolunteer. FIG. 13A has the subject in supine position and FIG. 13B hasthe subject in an upright position.

FIGS. 9A-9C show patient position and application of imaging coil andtracker coil for kinetic MR imaging of the knee. Patient is in uprightweight-bearing position for active flexion and extension study of theknee.

FIGS. 10A-10C show a 3D surface registration of femoral condyles basedon T1—weighted spin-echo MR images. FIG. 10A is a baseline with a kneeand neutral position. 10B is a follow-up with knee and external rotationwith a 3D view that is the identical to the one used in 10A but thedifference in knee rotation is apparent. In FIG. 10C, transformation andre-registration of Scan B into the object coordinate system of Scan Ashows the anatomic match to A can be excellent.

FIG. 11A shows a 2D cartilage thickness map where a proton density fastspin-echo MR image demonstrates a focal cartilage defect in theposterior lateral fermoral condyle (black arrows). White arrows indicateendpoints of the thickness map.

FIG. 11B is a 2D cartilage thickness map demonstrating abrupt decreasein cartilage thickness in the area of the defect (arrows). The Athickness between the neighboring pixels can be use to define theborders of the cartilage defect. Note defused cartilage thinning in thearea enclosed by the asterisks(*).

FIGS. 12A and 12B show the anatomic coordinate system in the femur andin the tibia.

FIG. 13 shows calculation of the anatomic coordinate system frompalpable bony landmarks.

FIGS. 14A and 14B show additional marker names and locations for MR tooptical cross registration.

FIGS. 15A and 15B show the marker names and locations for the standardpoint-cluster technique protocol.

FIGS. 16A-C show the error in the tibial location estimate for the rigidbody model and the intrical deformation correction technique.

FIGS. 17A-C shows the error in tibial orientation estimate for the rigidbody model and the interval deformation correction technique.

FIGS. 18A-18I show functional joint imaging.

FIG. 19 shows the superimposition of the tibiofemoral contact line ontothe 3D cartilage thickness map.

FIG. 20 shows the determination of the natural line of curvature as thecutting plain is rotated about the transepicondyear reference, thecartilage-plain intersection results in a curve.

FIG. 21 shows the determination of the tibiofemoral contact line throughthe proximity detection and approach algorithm.

FIGS. 22A and 22B show a 2D MRI (3D SPGR) and 3D cartilage thicknessmap.

FIGS. 23A-E show the matching of 3D thickness maps generated from MRimages obtained with a knee neutral position and external rotation.

FIGS. 24A-C show interpolation of the outer cartilage (OCS) surfaceacross a cartilage defect using the inner cartilage surface (ICS) as atemplate.

FIG. 24A shows that a volume of interest (VOI) is selected. Thecartilage volume within this VOI is measured. Two points P₁ and P₂ onthe OCS are selected on either side of the defect. The distances d₁ andd₂ to the ICS are measured.

FIG. 24B shows that the ICS-OCS distance values between P₁ and P₂ can bedetermined by means of a linear interpolation.

FIG. 24C shows that the interpolated OCS can be constructed using theinterpolated distance values.

DETAILED DESCRIPTION

Overview

FIG. 1 is a schematic overview of some of the various aspects of theinvention. While a complete description of the many aspects of theinvention is found in the specification and claims, the schematicoverview gives some of the broad aspects of the invention.

This invention relates to assessing the condition of a joint in amammal. One aspect is a method for such an assessment. The assessmentcan be done using internal images, or maps, of the cartilage alone or incombination with a movement pattern of the joint. If used alone, a mapobtained at an initial time is compared with a map obtained at a latertime to provide a view of the change in cartilage over time. Anotheraspect is a method is comparing the movement pattern for a joint of asubject being studied with the cartilage degeneration pattern of thesubject, then determining the relationship between the movement patternand the degeneration pattern. If, in determining the relationshipbetween the two patterns, one finds that the movement pattern has causedthe degeneration pattern or will continue to adversely affect thedegeneration pattern, therapy can be prescribed to minimize the adverseeffects, such as further degeneration or inflammation.

In overview, some of the systems and methods of this invention areillustrated by the flow chart in the attached FIG. 1. FIG. 1 is based onthe full range of processes, preferably applied to a knee andsurrounding cartilage.

In FIG. 1, the first step 10 represents obtaining an image of thecartilage itself. This is typically achieved using MRI techniques totake an image of the entire knee and then, optionally, manipulating(e.g., “subtracting out” or “extracting”) the non-cartilage images asshown in step 12. Non-cartilage images typically come from bone andfluid. Preferably, the MRI is taken using external markers to providereference points to the MRI image (step 11).

If the cartilage is imaged with a 2D MRI acquisition technique, theresulting stack of 2D images so obtained can be combined into a 3Dimage, as indicated in step 14. A preferred alternative is to use 3D MRIacquisition techniques to acquire a 3D image directly. In either case,the same “non-cartilage image extraction techniques referred to in step12 can be used.

With a full 3D image captured, various “maps” or displays of thecartilage can be constructed to give a cartilage degeneration pattern.This is represented by step 16. One such display can, for example, be acolor-coding of a displayed image to reflect the thickness for thecartilage. This will allow easy visual identification of actual orpotential defects in the cartilage.

Together with or independently of the cartilage imaging, and asrepresented by parallel step 20, a 3D image of the knee joint is taken,again preferably using MRI. Many of the same techniques as applied insteps 10 to 14 are used to do this. However, as illustrated by sub-step22, it is useful to define and register a skin-external frame ofreference around the joint. This is achieved by placing fiduciarymarkers on the skin around the outside of the knee (step 22) prior totaking the image.

In addition to an image extraction technique (as described above in step12), an image is manipulated to enhance the image of the position of themarkers (step 24). The resulting manipulated image is used to give a 3Dimage of the joint and associated bones (step 26).

With the markers in place, and as shown by step 30, an additional set ofmarkers is placed on the skin along the outside of the leg, and anexternal image of the limb is obtained. Using at least two cameras,images are then taken of the subject in a static state. In addition,images are also taken of the subject while moving. This is showncollectively by step 32. The images obtained are then processed torelate the movement of the skin relative to the bone. In addition,certain calculations are performed, for example, the center of mass iscalculated. These manipulations are shown in Step 34. Further, as thefiduciary markers are still in place during the video image capture, acorrelation between the fiduciary and the additional set of markers canbe made. This is shown in step 36.

Once this marker-to-marker correlation is made, the static 3D image ofthe joint (with associated fiduciary markers) and the movement images ofthe leg bones (also with fiduciary markers in place) can be combined.The fiduciary markers, therefore, serve as baseline references. Thecombination (step 40) of 3D cartilage image (from step 14), 3D kneejoint image (step 26), and the moving leg co-ordinates (step 34) will,after appropriate corrections, result in a displayable, 3D motion imageof the joint moving as per step 46.

The moving images, showing the contact areas of the knee joint can beused in conjunction with the various “maps” or displays generated atstep 16 to provide a visual indication of potential or actual cartilagedefects and help in determining their relation between movement anddegeneration patterns. This is shown in step 48.

Furthermore, as the various images are supported by actual mathematicalquantification, real measurements (such as cartilage thickness) can betaken and compared with later or earlier measurements and/or imaging.This allows the tracking of the progression of a defect, or conversely,continued tracking of healthy cartilage. This aids a health worker inproviding therapy for the patients. The method allows monitoring andevaluation of remedial actions as well as possible treatmentprescriptions.

Thus, this invention discloses, for example, a method to examine therelationship between articular cartilage morphology and the functionalload bearing areas of a knee joint measured during movement. The methodincludes enhanced imaging techniques to reconstruct the volumetric andbiochemical parameters of the articular cartilage in three dimensions;and a method for in vivo kinematic measurements of the knee. Thekinematic measurement permits direct in vivo measurements of completesix-degrees of freedom motion of the femur or the tibia or associatedbones during normal activities. This permits the study of load bearingof articular cartilage during movement. In particular, this method canaid in locating cartilage defects relative to the changing load bearingareas of the knee joint during daily activities. While the variousaspects of the invention are useful in mammals generally, they areparticularly useful for human patients.

Obtaining the Cartilage Degeneration Pattern

Imaging Articular Cartilage

In general, the joint of a patient is that place of union, more or lessmovable, between two or more bones. A joint comprises cartilage andother elements such as the accompanying bones on either side of thejoint, fluid, and other anatomical elements. Joints are classified intothree general morphological types: fibrous, cartilaginous, and synovial.This invention is particularly useful for assessing synovial joints,particularly the knee.

In obtaining an image of the cartilage of a joint in a mammal, a numberof internal imaging techniques known in the art are useful forelectronically generating a cartilage image. These include magneticresonance imaging (MRI), computed tomography scanning (CT, also known ascomputerized axial tomography or CAT), and ultrasound imagingtechniques. Others may be apparent to one of skill in the art. MRItechniques are preferred.

MRI, with its superior soft tissue contrast, is the best techniqueavailable for assessing tissue and its defects, for example articularcartilage and cartilage lesions, to obtain a cartilage degeneration canprovide morphologic information about the area of damage. Specifically,changes such as fissuring, partial or full thickness cartilage loss, andsignal changes within residual cartilage can be detected.

The reason MR imaging techniques are particularly suitable for cartilageis because they can provide accurate assessment of cartilage thickness,demonstrate internal cartilage signal changes, evaluate the subchondralbone for signal abnormalities, and demonstrate morphologic changes ofthe cartilage surface.

MRI provides several important advantages over other techniques in thisinvention. One advantage is good contrast between cartilage, bone, jointfluid, ligaments, and muscle in order to facilitate the delineation andsegmentation of the data sets. Another is the coverage of the entireregion of interest in a single scan within acceptable acquisition times.For a brief discussion of the basic MRI principles and techniques, seeMRI Basic Principles and Applications, Second Edition, Mark A. Brown andRichard C. Semelka, Wiley-Liss, Inc. (1999).

MRI employs pulse sequences that allow for better contrast of differentparts of the area being imaged. Different pulse sequences are betterfitted for visualization of different anatomic areas, for example,hyaline cartilage or joint fluid. More than one pulse sequence can beemployed at the same time. A brief discussion of different types ofpulse sequences is provided below.

High Resolution 3D MRI Pulse Sequences

Routine MRI pulse sequences available for imaging tissue, such ascartilage, include conventional T1 and T2-weighted spin-echo imaging,gradient recalled echo (GRE) imaging, magnetization transfer contrast(MTC) imaging, fast spin-echo (FSE) imaging, contrast enhanced imaging,rapid acquisition relaxation enhancement, (RARE) imaging, gradient echoacquisition in the steady state, (GRASS), and driven equilibrium Fouriertransform (DEFT) imaging. As these imaging techniques are well known toone of skill in the art, e.g. someone having an advanced degree inimaging technology, each is discussed only generally hereinafter. Whileeach technique is useful for obtaining a cartilage degeneration pattern,some are better than others.

Conventional T1 and T2—Weighted Spin-Echo Imaging

Conventional T1 and T2-weighted MRI depicts articular cartilage, and candemonstrate defects and gross morphologic changes. T1— weighted imagesshow excellent intra-substance anatomic detail of hyaline cartilage.However, T1-weighted imaging does not show significant contrast betweenjoint effusions and the cartilage surface, making surface irregularitiesdifficult to detect. T2-weighted imaging demonstrates joint effusionsand thus surface cartilage abnormalities, but since some components ofcartilage have relatively short T2 relaxation times, these are not aswell depicted as other preferred imaging.

Gradient-Recalled Echo Imaging

Gradient-recalled echo imaging has 3D capability and ability to providehigh resolution images with relatively short scan times. Fat suppressed3D spoiled gradient echo (FS-3D-SPGR) imaging has been shown to be moresensitive than standard MR imaging for the detection of hyalinecartilage defects in the knee.

Magnetization Transfer Contrast Imaging

Cartilage, as well as other ordered tissues, demonstrate the effects ofmagnetization transfer. Magnetization transfer imaging can be used toseparate articular cartilage from adjacent joint fluid and inflamedsynovium.

Fast Spin-Echo Imaging

Fast spin-echo imaging is another useful pulse sequence to evaluatearticular cartilage. Incidental magnetization transfer contrastcontributes to the signal characteristics of articular cartilage on fastspin-echo images and can enhance the contrast between cartilage andjoint fluid. Sensitivity and specificity of fast spin-echo imaging havebeen reported to be 87% and 94% in a study with arthroscopiccorrelation.

Contrast Enhanced Imaging

The use of gadolinium for imaging of articular cartilage has beenapplied in several different forms. Direct magnetic resonance (MR)arthrography, wherein a dilute solution containing gadolinium isinjected directly into the joint, improves contrast between cartilageand the arthrographic fluid. Indirect MR arthrography, with a lessinvasive intravenous injection, can also been applied. Gadoliniumenhanced imaging has the potential to monitor glycosaminoglycan contentwithin the cartilage, which may have implications for longitudinalevaluations of injured cartilage.

Driven Equilibrium Fourier Transform

Another 3D imaging method that has been developed is based on the drivenequilibrium fourier transform (DEFT) pulse sequence (U.S. Pat. No.5,671,741), and is specifically designed for cartilage imaging. DEFTprovides an effective tradeoff between T2/T1 weighting and spin densitycontrast that delineates the structures of interest in the knee.Contrast-to-noise ratio between cartilage and joint fluid is greaterwith DEFT than with spoiled gradient echo (SPGR). DEFT is an alternativeapproach to SPGR. DEFT contrast is very well suited to imaging articularcartilage. Synovial fluid is high in signal intensity, and articularcartilage intermediate in signal intensity. Bone is dark, and lipids aresuppressed using a fat saturation pulse. Hence, cartilage is easilydistinguished from all of the adjacent tissues based on signal intensityalone, which will greatly aid segmentation and subsequent volumecalculations.

The basic DEFT pulse sequence is shown in FIG. 2. A conventional spinecho pulse sequence was followed by an additional refocusing pulse toform another echo, and then a reversed, negated, excitation pulse toreturn any residual magnetization to the +z axis. This preserved themagnetization of longer T2 species, such as synovial fluid. Typical MRIparameters for cartilage are a T1-relaxation time of 900 Milliseconds(ms) and a T2-relaxation time of 40 ms, while synovial fluid has aT1-relaxation time of 3000 ms and a T2-relaxation time of 200 ms. Inaddition, synovial fluid has a 30% greater proton density thancartilage. The signal levels of cartilage and synovial fluid wereplotted in FIG. 3 for a RARE pulse sequence and for DEFT, and show thatDEFT maintains excellent contrast for any relaxation time (TR). Itachieves this contrast while maintaining a signal-to-noise ratio (SNR)efficiency (SNR)/(T_(acquisition))) that is equal to or better thanother methods with much lower contrast, such as T1-weighted GRASS.

DEFT was compared with a fast spin-echo (FSE), a gradient-echo (GRE),and a spoiled gradient-echo (SPGR) sequence with parameters similar tothe ones published by Disler et al. The patella was scanned in 10 normalvolunteer knees using a 1.5 T whole-body system (GE Signa) with a 3 inchsurface coil. All images were acquired with field of view (FOV) 10×10cm, matrix 256×256 elements, slice thickness 4 mm using fat-saturation.DEFT (400/15 [TR/TE in msec], 2 NEX (number of excitations), FSE(3500/15, echo train length [ETL] 8, 2 NEX (number of excitations), FSE(3500/15, ETL 4, 2 NEX), GRE (400/20, 30°, 2 NEX), and SPGR (50/15, 30°[flip angle], 2 NEX) images were obtained. Contrast-to-noise ratios(CNR) between cartilage and joint fluid were calculated as:CNR=|(SI _(Joint Fluid) −SI _(cartilage))/SI _(Background Noise)|  [Eq.1]

Contrast (C) between cartilage and joint fluid was calculated as:C=|[(SI _(joint Fluid) −SI _(cartilage))/SI _(Joint Fluid)]×100|  [Eq.2]

In the equations SI is signal intensity. DEFT demonstrated greatercontrast-to-noise ratio and contrast between cartilage and joint fluidthan SPGR, GRE, and FSE sequences (FIGS. 4 & 5). Cartilage hadintermediate signal intensity with DEFT, while joint fluid was high insignal intensity. The difference in CNR between DEFT and SPGR wasstatistically significant (p<0.001). Cartilage morphology, i.e.cartilage layers, were consistently best delineated with the DEFTsequence. At the resolution used in this study, FSE sequences sufferedfrom image blurring. Blurring was improved with ETL 4 when compared toETL8 nonetheless, even with ETL 4, cartilage morphology seen on FSEimages was inferior to the DEFT sequence. In light of these results,DEFT imaging is a preferred MRI technique.

Another Application of DEFT

DEFT was combined with a partial k-space echo-planar data acquisition.This pulse sequence is illustrated in FIG. 6 above. A slab selectivepulse in z defines the imaging volume, which is then resolved withphase-encoding gradients in the y and z axes, and an oscillating EPIgradient in the x axis.

Example images acquired with this approach are shown in FIG. 7. Thiscase was optimized for resolution, in order to image the patellarcartilage. The EPI readout acquired 5 echoes for each DEFT sequence.Partial k-space acquisition collected only 60% of the data along thex-axis. Correction for the missing data was performed using a homodynereconstruction. The image matrix was 192×192×32, with a resolution of0.5×0.5×2.5 mm, resulting in a 10×10×8 cm FOV. The echo time TE was 22ms, and the TR was 400 ms. Fat was suppressed with a fat presaturationpulse. The total scan time for this acquisition was 5 minutes.

Additional image studies that can be performed using this approach mayrequire greater spatial coverage, but one can permit slightly lessspatial resolution, and a longer scan time similar to the one used withthe 3D SPGR approach. If one relaxes the resolution to 0.75×0.75×1.5 mm,and doubles the z slab thickness and z phase encodes, the result will bea FOV of 15×15×16 cm, and a total scan time of approximately 15 minutes,which exactly fits the desired scan protocol. Similar to the 3D SPGRacquisition, one can acquire a first 3D DEFT scan in the sagittal planewith fat saturation. The 3D DEFT acquisition can then be repeatedwithout fat saturation using the identical parameters and slicecoordinates used during the previous acquisition with fat saturation.The resultant non-fat-saturated 3D DEFT images can be used for 3Drendering of the femoral and tibial bone contours.

In summary, Driven Equilibrium Fourier Transform is a pulse sequencepreferred for cartilage imaging that provides higher contrast-to-noiseratios and contrast between cartilage and joint fluid than SPGR, GRE,and FSE sequences. Cartilage morphology is better delineated with DEFTsequences than with SPGR, GRE, and FSE images. The combination of highanatomic detail and high cartilage-joint fluid CNR and contrast mayrender this sequence particularly useful for longitudinal studies ofcartilage in patients with osteoarthritis.

A Representative Example of MR Imaging is described below:

A MR image can be performed using a whole body magnet operating at afield strength of 1.5 T (GE Signa, for example, equipped with the GESR-120 high speed gradients [2.2 Gauss/cm in 184 μsec risetimes]). Priorto MR imaging, external markers filled with Gd-DTPA (Magnevist®, BerlexInc., Wayne, N.J.) doped water (T1 relaxation time approximately 1.0sec) can be applied to the skin around the knee joint and optionally atthe same positions used for gait analysis in a biomotion laboratory(discussed below). The external markers can be included in the field ofview of all imaging studies. Patients can be placed in the scanner insupine position. After an axial scout sequence, coronal and sagittalT1-weighted images of the femur can be acquired using the body coil(spin-echo, TR=500 msec, TE=15 msec, 1 excitation (NEX), matrix 256×128elements, field of view (FOV) 48 cm, slice thickness 7 mm, interslicespacing 1 mm). The scanner table can then be moved to obtain coronal andsagittal images of the knee joint and tibia using the same sequenceparameters. These T1-weighted scans can be employed to identify axesthrough the femur and tibia which can be used later for defining thegeometry of the knee joint. The knee can then be placed in the knee coilwith the joint space located in the center of the coil. The knee can besecured in the coil with padding. Additionally, the foot and ankleregion can be secured in neutral position to the scanner table usingadhesive tape in order to minimize motion artifacts. A rapid scout scancan be acquired in the axial plane using a gradient echo sequence(GRASS, 2D Fourier Transform (2 DFT), TR=50 msec, TE=10 msec, flip angle40°, 1 excitation (NEX), matrix 256×128 elements, field of view (FOV) 24cm, slice thickness 7 mm, interslice spacing 3 mm). This scout scan canbe used to demonstrate the position of the knee joint space in the coiland to prescribe all subsequent high resolution imaging sequencescentered over the joint space. Additionally, using the graphic, imagebased sequence prescription mode provided with the scanner software, thescout scan can help to ensure that all external markers around the kneejoint are included in the field of view of the high resolution cartilagesensitive MR sequences.

There are several issues to consider in obtaining a good image. Oneissue is good contrast between cartilage, bone, joint fluid, ligaments,and muscle in order to facilitate the delineation and segmentation ofthe data sets. Another is the coverage of both condyles of the knee in asingle scan within acceptable acquisition times. In addition, if thereare external markers, these must be visualized. One way to address theseissues is to use a three-dimensional spoiled gradient-echo sequence inthe sagittal plane with the following parameters (SPGR, 3 DFT,fat-saturated, TR=60 msec, TE=5 msec, flip angle 40°, 1 excitation(NEX), matrix 256×160 elements, rectangular FOV 16×12 cm, slicethickness 1.3 mm, 128 slices, acquisition time approximately 15 min).Using these parameters, one can obtain complete coverage across the kneejoint and the external markers both in mediolateral and anteroposteriordirection while achieving good spatial resolution and contrast-to-noiseratios between cartilage, bone and joint fluid (FIGS. 8 and 9). Thefat-saturated 3D SPGR sequences can be used for rendering the cartilagein three dimensions (see description below). The 3D SPGR sequence canthen be repeated in the sagittal plane without fat saturation using theidentical parameters and slice coordinates used during the previousacquisition with fat saturation. The resultant non-fat-saturated 3D SPGRimages demonstrate good contrast between low signal intensity corticalbone and high signal intensity bone marrow thereby facilitating 3Drendering of the femoral and tibial bone contours. It is to beunderstood that this approach is representative only and should not beviewed as limiting in any way.

Volumes of Interest (VOI)

The invention allows a health practitioner to determine cartilage lossin a reproducible fashion and thus follow the progression of a cartilagedefect over time.

In one embodiment of the invention, one can use a 2D or a 3D surfacedetection technique to extract the surface of the joint, e.g. thefemoral condyles, on both baseline and follow-up scans. For example, aT1-weighted spin-echo sequence can be used for surfaces extraction ofthe femoral condyles. The T1-weighted spin-echo sequence provides highcontrast between low signal intensity cortical bone and high signalintensity fatty marrow. For detection of the surface of the femoralcondyles, a step-by-step problem solving procedure, i.e., an algorithm,can convolve a data set with a 3D kernel to locate the maximum gradientlocation. The maximum gradient location corresponds to the zero crossingof a spatial location. When the kernel is designed properly, then therewill be only one zero crossing in the mask. Thus, that zero crossing isthe surface. This operation is preferably three-dimensional rather thantwo-dimensional. The surface of the joint, e.g. the femoral condyles, onthe baseline scan can be registered in an object coordinate system A.The surface of the joint, e.g. the femoral condyles, on the follow-upscan can be registered in an object coordinate system B. Once thesesurfaces have been defined, a transformation B to B′ can be performedthat best matches B′ with A. Such transformations can, for example, beperformed using a Levenberg Marquardt technique. Alternatively, thetransformations and matching can be applied to the cartilage only. Thesame transformation can be applied to the cartilage sensitive images onthe follow-up scan in order to match the cartilage surfaces.

Using the 3D surface registration of the joint on the baseline scan andresultant object coordinate system A, one can place volumes of interestover the area of a cartilage defect seen on the cartilage sensitiveimages. For example, in the knee joint, the size of the targeted volumesof interest can be selected to exceed that of the cartilage defect inanteroposterior and mediolateral direction, e.g. by 0.5 to 1 cm. If thedefect is located high on the femoral condyle or in the trochlearregion, the targeted VOI can be chosen so that its size exceeds that ofthe cartilage defect in superoinferior and mediolateral direction. Thethird dimension of the targeted VOI (parallel to the surface normal ofthe cartilage) can be fixed, for example at 1 cm. VOI size and placementcan be manual or automatic on the baseline study. Once the targeted VOIhas been placed on the image using visual or automated computer control,the 3D coordinates of the targeted VOI relative to the 3D contour of thejoint and object coordinate system A can be registered and saved. Onfollow-up studies, e.g. scans inadvertently obtained with slightlydifferent patient position, the 3D surface of the joint is registered tomatch the orientation of the baseline scan and the targeted VOI is thenautomatically placed on the joint using object coordinate system B′ andthe coordinates saved on the baseline study. Cartilage volume within thetargeted VOI on baseline and follow-up studies can, for example, bedetermined using standard thresholding and seed growing techniques.

Reference Markers

When obtaining the MR images for use in this invention, whether the MRIis of cartilage or of bone, external reference markers can be placed onthe skin around the joint of the subject being imaged. The externalmarker can be designed not only to show up in the MRI, but also to showup if an external image of the joint is obtained. The importance andvalue of such unique reference markers will be discussed in more detailhereinafter.

Thus, one embodiment of the invention is a skin reference marker thatcan be used in the assessment of the condition of a joint of a human.Multiple skin reference markers can be placed upon one or more limbs ofa patient prior to internal imaging and external imaging. Each skinreference marker comprises a material detectable by an imagingtechnique, a container for the material in which the containerpreferably has multiple surfaces, a means for affixing the container tothe skin (e.g. an adhesive placed on at least one surface of thecontainer in an amount sufficient to adhere the container to the skin ofa human), and a reflective material (preferably retro-reflective) placedon another surface of the container located away from the adhesive.Several imaging techniques can be used that are able to detect themarker. For example, magnetic resonance imaging is preferred, but,ultrasound, or X-ray are also useful. In the case of X-ray, furthermanipulations must be performed in which multiple X-ray images areassimilated by a computer into a 2 dimensional cross-sectional imagecalled a Computed Tomography (CT) Scan. The material detectable by animaging can be either in a liquid form or a solid form. The material canbe any imaging contrast agent or solution, e.g. a paramagnetic material.The material can be a lanthanide, such as one belonging to the yttriumgroup of rare earth metals. More specifically, the material can begadolinium. The shape of the container can be any shape allowing it tobe placed on the skin of a human. For example, it can be cubical,spherical, elliptical, discoid or cylindrical. The size of the containercan be any size, but optimally a size allowing it to be recorded by animaging machine. The longest dimension of the container can be up to 5.0cm, but preferably is about 0.25 to 2.0 cm. The reflective orretro-reflective material can be any material that is able to reflectlight directly back to the source of the light so that the position ofthe reference marker is captured by the opto-electrical recording means,e.g. a video camera. 3M Corporation makes several retro-reflectivematerials.

Manipulating Images

Once a magnetic resonance image is obtained, it can be manipulated toimprove the image by reducing unwanted, non-cartilage images.

Segmentation

To prepare the data set for 3D rendering, the cartilage can be segmentedimage by image using a signal-intensity-based threshold combined with aseed growing technique. The femoral, tibial, and patellar cartilage canbe segmented separately based on the fat-saturated 3D SPGR or 3D DEFTsequence. Manual disarticulation can be performed by outlining thecartilage contour in areas where the signal intensity of the articularcartilage is similar to that of adjacent structures. The contours of thefemoral, tibial, and patellar bone can be segmented separately using thenon-fat-saturated 3D SPGR or 3D DEFT sequence. Segmentation software canallow for manual editing of cartilage thickness maps and cartilagedefects detected using the above embodiments. In this fashion, theoperator can correct erroneous detection of cartilage defects in areaswhere the cartilage may be naturally thinner. Such software includesseed-growing algorithms and active-contour algorithms that are run onstandard PC's. A sharp interface is present between the high signalintensity bone marrow and the low signal intensity cortical bone therebyfacilitating seed growing. Fat-saturated and non-fat-saturated 3Dsequences can be acquired with the same field of view, slice thicknessand slice positions, thereby enabling superimposition and crossregistration of any resultant 3D renderings of the femoral, tibial, andpatellar cartilage and bone. External reference markers can aid inregistering the 3D data in the same object coordinate system.

3D maps of cartilage thickness can be generated using several differenttechniques. One representative, but not limiting, approach uses a 3Dsurface detection technique which is based on a 2D edge detector(Wang-Binford) that has been extended to 3D. This surface detectiontechnique can generate surface points and their corresponding surfacenormal. To smooth the contour, the program samples 25 percent of thesurface points and fits a cubic spline to the sample points. The programcan compute the curvature along sample spline points and find two samplepoints that have the maximum curvature and are separated by about halfthe number of voxels on the contour. These points partition the splineinto two subcontours. For each subcontour, the program can compute theaverage distance between the points and the center of the mass. Theprogram can designate the subcontour with the smaller average distanceas the inner cartilage surface and the other subcontour as the outercartilage surface (OCS). The intersect between the inner cartilagesurface (ICS) (located at the subchondral bone interface) and the outercartilage surface with the surface normal can be used to compute the 3Dthickness of the articular cartilage on a pixel-by-pixel basis.

Creating a Three Dimensional (3D) Image of the Cartilage

Three Dimensional Geometric Model Generation

After the 3D image of cartilage and the 3D image of joint with bones (asdiscussed hereinafter), are obtained, for example, the set of segmentedtwo dimensional MR images can be transformed to a voxel representationusing a computer program developed in the AVS Express (Advanced VisualSystems, Inc., Waltham, Mass.). Every voxel has a value of zero if it isnot within an object of interest or a value ranging from one to 4095,depending on the signal intensity as recorded by the MRI machine. Anisosurface can then be calculated that corresponds to the boundaryelements of the volume of interest. A tesselation of this isosurface canbe calculated, along with the outward pointing normal of each polygon ofthe tesselation. These polygons are written to a file in a standardgraphics format (Virtual Reality Modeling Language Version 1.0: VRMLoutput language).

Visualization Software

One possible choice for the software program used to assess thecartilage degeneration pattern, the bones of the joint, and the motionpattern of the patient is a user controllable 3D visual analysis tool.The program can read in a scene, which scene consists of the various 3Dgeometric representations or “actors” (for example, VRML files of thetibia, tibia cartilage, femur, femoral cartilage), the staticrelationship transformations between these actors, and, if available,sequence of transformations describing how these actors move withrespect to each other as the patient performs some activity, such aswalking, jogging, etc.

The program can allow the user, through the use of the mouse and/orkeyboard, the ability to observe the scene from arbitrary angles; tostart and stop the animation derived from the motion profiles and toobserve the contact line and any cartilage lesions while the animationis running. Additionally, the user can derive quantitative informationon the scene through selecting points with the mouse.

The software program can be written in the CTT computer language and canbe compiled to run on both Silicon Graphics Workstations andWindows/Intel personal computers.

Cartilage Thickness Maps

Cartilage thickness can be determined by several methods. One example isdetecting the locations of the bone-cartilage and the cartilage-jointfluid interface along the surface normal using the same edge detectordescribed below, and subtracting them. This procedure can be repeatedfor each pixel located along the bone-cartilage interface. The x, y, andz position of each pixel located along the bone-cartilage interface canbe registered on a 3D map or multiple 2D maps and thickness values aretranslated into color values. In this fashion, the anatomic location ofeach pixel at the bone cartilage interface can be displayedsimultaneously with the thickness of the cartilage in this location.

The edge detector can produce accurate surface points and theircorresponding surface normal. The detector can be applied to thebaseline and the follow-up data set. For the baseline data set, both thesurface points and surface normals can be used to form locallysupporting planes (for each voxel). These planes can form anapproximated surface for the baseline skeletal site. As for thefollow-up data set, the surface points can be matched in theregistration procedure onto the surface of the baseline data set. Onecan use a newly developed 3D surface detection technique to extract thesurface of the skeletal site on both the baseline scan and the follow-upscan. Once these surfaces are detected, one can use the LevenbergMarquardt procedure to find the transformation that best matches thesetwo surfaces.

A possible approach for calculating the cartilage thickness is based ona 3D Euclidian distance transformation (EDT). After thresholding, thevoxels on the edge of the cartilage structure can be extracted using aslice by slice 8-neighbor search, resulting in a binary volume with thevoxels on the cartilage surface having a value of 1 and all others being0. To classify these surface points as part of the ICS or OCS, asemi-automatic approach, which requires the user to enter a point thatlies outside the cartilage structure and faces the ICS, can be useful.From this point, rays are cast in all directions of the volume using amodified Bresenham's line drawing algorithm. If a ray hits a voxel witha value of 1, this point is classified as part of the ICS: After acomplete sweep of the volume, for initialization of the EDT the ICSvoxels are given a value of 0, whereas all other voxels are set to 1.

For computation of the EDT, the following representative algorithm canbe useful. It can decompose the calculation into a series of 3one-dimensional transformations and can use the square of the actualdistances, which accelerates the process by avoiding the determinationof square roots.

First, for a binary input picture F={f_(ijk)} (1≦i≦L, 1≦j≦M, 1≦k≦N) anew picture G={g_(ijk)} can be derived using equations (3-5) (α, β, andγ denote the voxel dimensions). Here F is a set of all voxels initiallyand G is a set of all voxels at the later time.g _(ijk)=_(x)min{(α(i−x))² ;f _(xjk)=0;1≦x≦L}  [Eq. 3]

Thus, each point can be assigned the square of the distance to theclosest feature point in the same row in i-direction. Second, G can beconverted into H={h_(ijk)} using equation (4).h _(ijk=y)min{g _(iyk)+(β(j−y))²;1≦y≦M}  [Eq. 4]

The algorithm can search each column in the j-direction. According tothe Pythagorean theorem, the sum of the square distance between a point(i,j,k) and a point (i,y,k) in the same column, (β(j−y))², and thesquare distance between (i,y,k) and a particular feature point, g_(iyk),equals the square distance between the point (i,j,k) and that featurepoint. The minimum of these sums is the square distance between (i,j,k)and the closest feature point in the two-dimensional i-j-plane.

The third dimension can be added by equation (5), which is the sametransformation as described in the equation for the k-direction (4).s _(ijk) = _(z)min{h _(ijz)+(γ(k−z))²; 1≦z≦N}  [Eq. 5]

After completion of the EDT, the thickness of the cartilage for a givenpoint (a,b,c) on the OCS equals the square root of s_(abc). The x, y,and z position of each pixel located along the bone-cartilage interfacecan be registered on a 3D map and thickness values are translated intocolor values. In this fashion, the anatomic location of each pixel atthe bone cartilage interface can be displayed simultaneous with thethickness of the cartilage in this location.

Displaying the Degeneration Pattern

In an approach the cartilage thickness maps obtained using the algorithmdescribed above display only a visual assessment of cartilage thicknessalong the articular surface. In another approach, in order to derive atrue quantitative assessment of the location, size, and depth of a focalcartilage defect, one can use an iterative approach comparing cartilagethickness of neighboring pixels located along the bone cartilageinterface.

For example, assuming an image resolution of 0.5×0.5×1.0 mm and anaverage thickness of the articular cartilage in the femoral condylesranging between 2 to 3 mm, a 25% decrement in cartilage thickness willbe the smallest change that can be observed with most current imagingsequences. Therefore, for example, pixels along the bone—cartilageinterface that demonstrate a decrease exceeding the smallest changeobservable on a given MRI pulse sequence, in this example 25% orgreater, in overlying cartilage thickness when compared to cartilagethickness at the neighboring bone—cartilage interface pixels, can beused to define the margins of a focal cartilage defect. Other criteriacan be employed to define a cartilage defect based on comparisons ofneighboring pixels. For example, a fixed value can be used. If thedifference in cartilage thickness between neighboring pixels exceeds thefixed value, e.g. 1 mm, the pixel where this difference is observed canbe used to define the margin of the cartilage defect. This comparisoncan be performed for each pixel located along the bone—cartilageinterface for the entire data set. This comparison is preferablyperformed in three dimensions. Pixels that demonstrate a decrease incartilage thickness exceeding defined criteria but that are completelysurrounded by other pixels fulfilling the same criteria may not beconsidered to be part of the margin of the cartilage defect, but willtypically be considered to lie inside the cartilage defect.

The invention provides for means for calculating the area covered by thecartilage defect A_(cartilage defect) and the mean thickness of thecartilage in the region of the defect D_(cartilage defect) as well asthe mean thickness of a defined area of surrounding normal cartilage.The thickness of the cartilage previously lost in the defect can beestimated then as:D _(cartilage loss) =D _(normal cartilage) −D_(cartilage defect)  [Eq.6]

Since the area A of the cartilage defect is known, the volume ofcartilage loss can be computed as:V _(cartilage loss) =A _(cartilage defect) ×D _(cartilage loss)  [Eq. 7]

Thus, the invention provides for means of estimating the thickness, areaor volume of cartilage tissue that has been lost.

In another embodiment, the cartilage is segmented slice by slice from MRimages. This can be achieved, for example, using the live wire method orsnakes. After segmentation, a volume of interest (VOI) containing asingle cartilage defect can be selected. The cartilage volume V₁ withinthis VOI can be determined. In each slice that contains the defect, twopoints P₁ and P₂ on the outer cartilage surface (OCS) can be selected oneither side of the defect (FIG. 24, A). The OCS contour of the cartilagedefect between P₁ and P₂ can be erased and interpolated using the innercartilage surface (ICS) as a guiding line. For this purpose, thedistances d₁ and d₂ between the OCS at P₁ and P₂ and the ICS can bemeasured. For OCS reconstruction the OCS-ICS distance can be determinedby linear interpolation between d₁ and d₂ (FIG. 24, B). The interpolateddistance values can be used to determine a set of interpolated surfacepoints for the reconstructed OCS (FIG. 24, C). The surface contourbetween P₁ and P₂ can be determined with a spline curve thatinterpolates this set of OCS points. Subsequently, the cartilage volumeV₂ can be measured, using the same VOI as for VI. The difference V₂−V₁between the two volumes can yield the volume of the cartilage defect.

The depth of the cartilage defect can, for example, be determined asfollows: for all points on the interpolated OCS in all the slicescontaining the defect contour the distance to the closest point of theoriginal OCS can be measured by means of a 3-dimensional Euclideandistance transform. The longest distance value resulting from thiscomputation is typically the depth of the cartilage defect.

The area of the cartilage defect can, for example, be determined asfollows: for all the slices that contain the defect, the length of theinterpolated OCS can be computed. The sum of these length values,multiplied by the slice thickness, can yield an estimation of the totalarea of the interpolated OCS contour and thus the area of the cartilagedefect.

In another embodiment, the invention provides for means to directlycompare the volume, depth, and area of an articular cartilage defectbetween different MR examinations without having to register the datasets. Thus, the invention can be used to monitor the progression ofosteoarthritis. As an additional example of how this technique can beapplied, the invention can be used to monitor the effect of diseasetherapy. In another embodiment, the invention can be used to collectepidemiological data on the volume, depth, and area of articularcartilage defects in different locations of the femur, tibia, andpatella.

The invention provides means to accurately measure the volume, depth,and area of a cartilage defect. Furthermore, the comparison between thevalues for different MR examinations can be performed withoutregistration of the data sets.

Turning now to FIGS. 22A and 22B, one can see a 2D MRI (3D SPGR) and 3Dcartilage thickness map. In A, the 2D MRI demonstrates a full thicknesscartilage defect in the posterior lateral femorl condyle (arrows). FIG.22B shows a 3D cartilage thickness map generated using a 3D Euclidiandistance transformation. The thickness of the articular cartilage iscolor encoded and displayed on a pixel-by-pixel basis along the 3Dsurface of the articular cartilage. The cartilage defect is blackreflecting a thickness of zero (arrows) (M: medial, L: lateral, S:superior, I: inferior).

In FIGS. 23A-23E, one can see the matching of 3D thickness mapsgenerated from MR images obtained with the knee in neutral position andin external rotation. A. Sagittal baseline MR image (3D SPGR) with theknee in neutral position. B. Sagittal follow-up MR image of the samevolunteer obtained two weeks later with the knee in 40 degree externalrotation (note the artificially widened appearance of the femurresulting from the rotation). C. 3D thickness map generated based onbaseline MRI in neutral position. D. 3D thickness map generated based onfollow-up MRI in external rotation (note segmentation error betweencondyles in trochlear region). E. Transformation of D into the objectcoordinate system of C. Despite extreme differences in joint orientationbetween baseline and follow-up MRI scans and despite segmentationerrors, the thickness distribution on the matched follow-up scandemonstrates great similarity with that seen on the baseline scan inneutral position (in C.).

Having now described how to obtain an image of a cartilage of a joint,both with and without external reference markers; how to enhance theimage by manipulating non-cartilage images, and creating and displaying3-D images of the cartilage, ie. a 3-D map, certain aspects of theinvention are apparent.

One aspect is a method of estimating the loss of cartilage in a joint.The method comprises

(a) obtaining a three-dimensional map of the cartilage at an initialtime and calculating the thickness or regional volume of a regionthought to contain degenerated cartilage so mapped at the initial time,

(b) obtaining a three-dimensional map of the cartilage at a later time,and calculating the thickness or regional volume of the region thoughtto contain degenerated cartilage so mapped at the later time, and

(c) determining the loss in thickness or regional volume of thecartilage between the later and initial times.

Preferably, this aspect of the invention is directed to a volume ofinterest in the cartilage, i.e., a region of the cartilage that includesa cartilage defect. Such a defect may be the result of a disease of thecartilage (e.g., osteoarthritis) or the result of degeneration due tooveruse or age. This invention allows a health practitioner to evaluateand treat such defects. The volume of interest may include only theregion of cartilage that has the defect, but preferably will alsoinclude contiguous parts of the cartilage surrounding the cartilagedefect.

Another aspect of the invention is a method for assessing the conditionof cartilage in a joint of a human, which method comprises

(a) electronically transferring an electronically-generated image of acartilage of the joint from a transferring device to a receiving devicelocated distant from the transferring device;

(b) receiving the transferred image at the distant location;

(c) converting the transferred image to a degeneration pattern of thecartilage; and

(d) transmitting the degeneration pattern to a site for analysis.

Another aspect of the invention is a method for determining the volumeof cartilage loss in a region of a cartilage defect of a cartilage injoint of a mammal. The method comprises (a) determining the thickness,DN, of the normal cartilage near the cartilage defect; (b) obtaining thethickness of the cartilage defect, DD, of the region; (c) subtracting DDfrom DN to give the thickness of the cartilage loss, DL; and (d)multiplying the DL value times the area of the cartilage defect, AD, togive the volume of cartilage loss. The method is useful for situationswherein the region of cartilage defect is limited to the defectivecartilage and preferably wherein the region of the cartilage defectincludes a portion of the cartilage contiguous to the defect.Alternatively, for step (a) the normal thickness of the defect areacould be estimated. It may be estimated from measurements of cartilageof other subjects having similar characteristics such as gender, age,body type, height, weight, and other factors. It may be estimated frommeasurements of a similar “normal” cartilage from another correspondingjoint (e.g., if the right knee has the defect, measure the normal leftknee). It may have been measured at an initial time T₁ when thecartilage was normal to provide a baseline. Other means of determiningthe normal thickness may be available to one of skill in the art. Oncethe thickness D_(N) is obtained and the thickness D_(D) is obtained thetwo are subtracted to give the D_(L). The D_(L) is multiplied by thearea of the defect A_(D) to give the volume of cartilage loss. Bydetermining the volume of cartilage loss at an initial T₁ and again at alater time T₂, one can determine the change in volume loss over time.

Still another aspect of the invention is a method of estimating thechange of a region of cartilage in a joint of a mammal over time. Themethod comprises (a) estimating the thickness or width or area or volumeof a region of cartilage at an initial time T₁, (b) estimating thethickness or width or area or volume of the region of cartilage at alater time T₂, and (c) determining the change in the thickness or widthor area or volume of the region of cartilage between the initial and thelater times. The method is particularly useful for regions ofdegenerated cartilage or diseased cartilage.

Still another aspect of the invention is a method of estimating the lossof cartilage in a joint. The method comprises (a) defining a 3D objectcoordinate system of the joint at an initial time, T₁; (b) identifying aregion of a cartilage defect within the 3D object coordinate system; (c)defining a volume of interest around the region of the cartilage defectwhereby the volume of interest is larger than the region of cartilagedefect, but does not encompass the entire articular cartilage; (d)defining the 3D object coordinate system of the joint at a secondtimepoint, T₂; (e) placing the identically-sized volume of interest intothe 3D object coordinate system at timepoint T₂ using the objectcoordinates of the volume of interest at timepoint T₁; (f) and measuringany differences in cartilage volume within the volume of interestbetween timepoints T₁ and T₂.

Therapeutic Planning, Devising New Therapies

In another embodiment of the invention, thickness of the articularcartilage can be estimated using an imaging test. This imaging test canbe an x-ray, ultrasound, CT scan or MRI scan. Thickness can bedetermined using a 3D Euclidian distance transformation as well as othertechniques feasible for this purpose. Thickness can be determined inselected regions or across the entire cartilage of a joint. Thicknesscan be determined in areas adjacent to diseased cartilage, in areas ofdiseased cartilage and in areas of normal cartilage.

Furthermore, the curvature of the cartilage can be determined. For thispurpose, the curvature of the inner cartilage surface, i.e. the surfacefacing the subchondral bone, or the outer cartilage surface, i.e. thesurface facing the joint cavity, can be determined. Preferably, theinner cartilage surface at the interface with the subchondral bone willbe used, since the outer cartilage surface may be subject to fraying,fissuring or more advanced stages of cartilage loss. Alternatively, thecurvature of the subchondral bone can be determined. In this case, thecurvature of the subchondral bone can serve as an approximation of thecurvature of the articular cartilage. Curvature can be determined froman imaging test, typically an ultrasound, a CT or an MRI scan. Curvaturecan be determined from a three-dimensional model of the cartilage. Thethree-dimensional model of the cartilage can be determined using the 3DEuclidian distance transformation mentioned above. Curvature can bedetermined in selected regions or across the entire cartilage of ajoint. Curvature can be determined in areas adjacent to diseasedcartilage, in areas of diseased cartilage and in areas of normalcartilage.

Using information on thickness and curvature of the cartilage, aphysical model of the surfaces of the articular cartilage and of theunderlying bone can be created. This physical model can berepresentative of a limited area within the joint or it can encompassthe entire joint. For example, in the knee joint, the physical model canencompass only the medial or lateral femoral condyle, both femoralcondyles and the notch region, the medial tibial plateau, the lateraltibial plateau, the entire tibial plateau, the medial patella, thelateral patella, the entire patella or the entire joint.

In another embodiment, the location of a diseased area of cartilage canbe determined, for example using a 3D coordinate system or a 3DEuclidian distance transformation in combination with some of thetechniques outlined above. In another embodiment of the invention, theanteroposterior, mediolateral or superoinferior dimension of an area ormultiple areas of diseased cartilage can be determined. Furthermore, thearea, depth and volume of a cartilage defect can be determined, forexample using a 3D Euclidian distance transformation in combination withsome of the techniques outlined above

In one embodiment of the invention, information on thickness of thecartilage, information on curvature of the cartilage, information oncurvature of the subchondral bone or information on the location,dimensions, area, depth and volume of a defect or combinations thereofcan be used to devise a treatment. For example, the dimensions of adefect determined in this manner can be used to determine the dimensionsof a cartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. Additionally, thecurvature of the inner cartilage surface or the subchondral bone can bemeasured and this information can be used to determine the shape of acartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. Additionally, thethickness of normal cartilage adjacent to the defect can be measured andthe thickness values measured in this fashion can be used to determinethe optimal thickness for a cartilage transplant, a cartilage graft, acartilage implant, a cartilage replacement material, a cartilagescaffold or a cartilage regenerating material or any cartilage repairsystem. Alternatively, the thickness of the cartilage can be measured inthe contralateral joint, e.g. the knee joint, in an area correspondingto the area of diseased cartilage in the affected joint. Using any ofthese techniques or, preferably, a combination thereof, an optimal fitcan be achieved between the surrounding normal cartilage and a cartilagetransplant, a cartilage graft, a cartilage implant, a cartilagereplacement material, a cartilage scaffold or a cartilage regeneratingmaterial or any cartilage repair system thereby minimizing incongruityat the joint surface and improving the therapeutic result.

The invention provides for means to create a cast or a mold for shapinga cartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. This can begenerated using computer devices and automation, e.g. computer assisteddesign (CAD) and, for example, computer assisted modeling (CAM).

In another embodiment, the invention provides for means to measure andvisualize the curvature of the surfaces of cartilage and bone. Inanother embodiment, the invention provides for means to compare thethickness and the curvatures of surfaces of a cartilage transplant and atransplantation site, a cartilage graft and a graft site, a cartilageimplant and an implantation site, a cartilage replacement material andan implantation site, a cartilage scaffold and a cartilage defect, acartilage regenerating material and an area of diseased cartilage, or acartilage repair system and an area of diseased cartilage.

The invention is useful for determining the shape, dimensions andthickness of a cartilage transplant, a cartilage graft, a cartilageimplant, a cartilage replacement material, a cartilage scaffold or acartilage regenerating material or any cartilage repair system prior totreatment. For example, the shape, dimensions and thickness of acartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system can be designed tofollow the physical shape and thickness of the cartilage adjacent to anarea of diseased cartilage determined using the imaging test.

The invention is applicable to a host of current and future treatmentsof arthritis including but not limited to cartilage transplants,cartilage implants, cartilage grafts, cartilage replacement materials,cartilage scaffolds, cartilage regenerating materials, auto-, allo- andxeno-transplants, osteochondral allo- and autografting, stem cell basedrepair systems and transfer systems, and, principally, any other currentand future treatments and devices for cartilage repair or regeneration.

The example described below shows one possibility how aspects of theinvention can be implemented. It demonstrates one possible way how theinvention can be practiced. It is in no way meant to limit theinvention. One skilled in the art will easily recognize other means ofpracticing the invention.

In a first step, the cartilage and bone contours can be segmented fromthe MR images using for example a modified live wire technique [SteinesD, Cheng C, Wong A, Berger F, Napel S, Lang P. CARS—Computer-AssistedRadiology and Surgery, p. 578-583, San Francisco, 2000].

From the segmented data, a 3-dimensional surface representation can becreated, which yields a triangular tesselation of the surface. For thiscalculation, a 3-dimensional Wang-Binford edge detector [Yan C H:Measuring changes in local volumetric bone density: new approaches toquantitative computed tomography, Ph.D. thesis, 1998, Dept. ofElectrical Engineering, Stanford University] or the marching cubesalgorithm [Lorensen W E, Cline H E. Comput Graph 1987; 21: 163-169] canbe used. This surface representation can be imported into a CAD system,which is used to generate a physical model or a cast by means of rapidprototyping techniques.

The segmented data can also be used for measuring the surface curvatureat the surface points. The curvature is calculated according to formulaxx. If α:(a,b)→IR² is a curve defined over the parameter interval (a,b)by α(t)=(x(t),y(t)), then the curvature K is given by [Gray A: ModernDifferential Geometry of Curves and Surfaces. 1993; CRC Press]:κ(t)=[x′(t)y″(t)−x″(t)y′(t)]/[(x′ ²(t)+y′ ²(t))^(3/2)]  [Eq. 8]

For the digitized contours that result from the segmentation, smoothderivatives for equation (8) can be obtained by convolution withdifferentiated Gaussian kernels [Worring M, Smeulders A W M. CVGIP:Image Understanding, 1993. 58(3): p. 366-382].

The curvature values can be calculated for each pixel on the segmentedsurface in each slice. They can subsequently be color-mapped onto a3-dimensional rendering of the surface for visualization purposes.

For full curvature information this procedure can be repeated for adirection perpendicular to the imaging plane. Of the remaining two maindirections the one with the lower degree of parallelism to the surfacecan be chosen, and reformatted slices of the MR data set can be obtainedfor this direction. For instance, when curvature values for the femoralcondyles are calculated from sagittal MR images of the knee, the dataset would typically be reformatted for a coronal slicing plane.

The procedure of segmentation and curvature calculation can then berepeated for the reformatted images. A second curvature map can becalculated, yielding complementary information to the first one.

In order to compare curvature values for different surface patches,these can be manually or automatically registered, overlaying onesurface patch on top of the other. Corresponding curvature values thatare calculated in the same directions can now be subtracted from eachother, thereby yielding a measure of how well curvatures of two surfacesmatch.

An example how aspects of the invention can be practiced clinically in apatient is given below. It demonstrates one possible way how theinvention can be practiced. It is in no way meant to limit theinvention. One skilled in the art will easily recognize other means ofpracticing the invention.

A patient with arthritis of the knee joint is referred for an MRI scan.The MRI scan is performed using a cartilage sensitive MRI pulse sequencesuch as a fat saturated spoiled gradient echo sequence or a waterselective 3D gradient echo sequence using a spectral spatial pulse. TheMR images are transferred via a local network or, for example, theinternet into a computer workstation. The computer workstation usessoftware to extract or segment the articular cartilage from thesurrounding tissue. Such software can include snake algorithms, livewirealgorithms, signal intensity based thresholding, or seed growingalgorithms as well as any other technique useful for this purpose. Thesoftware can then generate a three-dimensional map of cartilagethickness across the femoral condyles, the tibial plateau, or thepatella. This can be achieved using a 3D Euclidian distancetransformation. Additionally, the software can provide information oncartilage curvature or curvature of the subchondral bone as describedabove. Furthermore, the software can determine the location, dimensions,size, area, depth, or volume of areas of diseased cartilage.

The information generated in this fashion can be used to generate aphysical model of the area of diseased cartilage. For example, a bonereplacement material can be formed with a CAD/CAM system using the aboveinformation. One of the surfaces of the bone replacement material can beshaped so that it matches the 3D curvature of the subchondral bonesubjacent to the area of diseased cartilage. Additionally, theanteroposterior, mediolateral or superoinferior dimensions of thissurface of the bone replacement material can be such that it matches thedimensions of the area of diseased cartilage. Cartilage cells can beaffixed to the bone replacement material and can be grown on the bonereplacement material until the thickness of the resultant cartilagematches that of the thickness of the cartilage adjacent to the area ofdiseased cartilage measured on the 3D cartilage thickness map.Alternatively, a layer of cartilage of known thickness can be applied tothe bone replacement material whereby the thickness can be chosen tomatch the thickness of the cartilage adjacent to the area of diseasedcartilage measured on the 3D cartilage thickness map.

Alternatively, an artificial non-human material with properties similarto cartilage can be applied to the bone replacement material whereby thethickness of this material can be chosen to match the thickness of thecartilage adjacent to the area of diseased cartilage measured on the 3Dcartilage thickness map.

Alternatively, cartilage can be grown on a mold matching the curvatureof the subchondral bone in an area of diseased cartilage whereby thedimensions of the surface of. the mold on which the cartilage is grownmatches the dimensions of an area of diseased cartilage. Cartilage canthen be grown on the mold until its thickness matches the thickness ofcartilage adjacent to the area of diseased cartilage as measured, forexample, on a 3D cartilage thickness map. At this point, for example, anorthopedic surgeon can excise the area of diseased cartilage and canimplant the cartilage or cartilage replacement material. Since thecurvature of the cartilage or cartilage replacement material matchesthat of the underlying subchondral bone and since the thickness of thecartilage or cartilage replacement material matches that of thecartilage adjacent to the area of excised diseased cartilage, normal ornear normal joint congruity can be achieved with a resultant decrease inwear on the implanted cartilage or cartilage replacement material andalso a decrease in wear on the adjacent cartilage or the cartilage ofthe opposing joint surface.

Display of Biochemical Information

In addition to providing a 2D or 3D representation of the morphologicalproperties of cartilage, the invention provides for techniques torepresent one or more biochemical components of articular cartilage.

A biochemical component includes, but is not limited to,glycosaminoglycan, water, sodium, or hyaluronic acid. Biochemical datacan be generated with other magnetic resonance based techniquesincluding the use of signal intensity measurements, relaxation timemeasurements, paramagnetic and other contrast media and sodium ratherthan proton MR imaging. Other imaging tests such as positron emissiontomography scanning can also be used for this purpose. Thus, one aspectof this invention is a method for providing a biochemically-based map ofjoint cartilage. The method comprises

(a) measuring a detectable biochemical component throughout thecartilage,

(b) determining the relative amounts of the biochemical componentthroughout the cartilage;

(c) mapping the amounts of the biochemical component through thecartilage; and

(d) determining the areas of cartilage deficit by identifying the areashaving an altered amount of the biochemical component present.

Once a map is obtained, it can be used in assessing the condition of acartilage at an initial time and over a time period. Thus, thebiochemical map may be used in the method aspects of the invention in amanner similar to the cartilage thickness map.

For example, one aspect is a method of estimating the loss of cartilagein a joint. The method comprises

(a) obtaining a relaxation time or biochemical map of the cartilage atan initial time and analyzing the relaxation time or biochemical contentof a region thought to contain degenerated cartilage so mapped at theinitial time,

(b) obtaining a relaxation time or biochemical map of the cartilage at alater time, and time analyzing the relaxation time or biochemicalcontent of the region thought to contain degenerated cartilage so mappedat the later time, and

(c) determining the change in relaxation time or biochemical content ofthe cartilage between the later and initial times.

Preferably, this aspect of the invention is directed to a volume ofinterest in the cartilage, i.e., a region of the cartilage that includesa cartilage defect. Such a defect may be the result of a disease of thecartilage (e.g., osteoarthritis) or the result of degeneration due tooveruse or age. This invention allows a health practitioner to evaluateand treat such defects. The volume of interest may include only theregion of cartilage that has the defect, but preferably will alsoinclude contiguous parts of the cartilage surrounding the cartilagedefect.

As discussed herein before, another aspect of the invention is a methodfor assessing the condition of cartilage in a joint using a relaxationtime or the biochemical map. The method comprises

(a) electronically transferring an electronically-generated relaxationtime or biochemically based image of a cartilage of the joint from atransferring device to a receiving device located distant from thetransferring device;

(b) receiving the transferred image at the distant location;

(c) converting the transferred image to a degeneration pattern of thecartilage; and

(d) transmitting the degeneration pattern to a site for analysis.

Another aspect of the invention is a method for determining the changeof biochemical content in a region of a cartilage defect of a cartilagein joint of a mammal. The method comprises (a) determining thebiochemical content (BC_(N)) of the normal cartilage near the cartilagedefect; (b) obtaining the biochemical content of the cartilage defect(BC D) of the region; and (c) subtracting BC_(D) from BC_(N) to give thevalue of the cartilage change, BC_(D). The method is useful forsituations wherein the region of cartilage defect is limited to thedefective cartilage and preferably wherein the region of the cartilagedefect includes a portion of the cartilage contiguous to the defect.

Alternatively, for step (a) the normal content of the defect area couldbe estimated. It may be estimated from measurements of cartilage ofother subjects having similar characteristics such as gender, age, bodytype, height, weight, and other factors. It may be estimated frommeasurements of a similar “normal” cartilage from another correspondingjoint (e.g., if the right knee has the defect, measure the normal leftknee). It may have been measured at an initial time T₁ when thecartilage was normal to provide a baseline. Other means of determiningthe normal content may be available to one of skill. in the art. OnceBC_(N) is obtained and BC_(D) is obtained the two are subtracted to givethe Δ. By determining the change of content at an initial T₁ and againat a later time T₂, one can determine the change in biochemical contentover time.

Once a relaxation time or biochemically-based map is provided,morphological maps of articular cartilage obtained with MR imaging canbe superimposed, merged or fused with the biochemical map or data.Several different techniques can be applied in order to superimpose,merge, or fuse morphological data with biochemical data. For example, 2Dor 3D morphological data of articular cartilage can be acquired with thesame object coordinates as the biochemical data. Morphological data andbiochemical data can then be easily displayed simultaneously usingdifferent colors, opacities, and or gray scales. Alternatively, 2D or 3Dmorphological data or articular cartilage can be acquired with differentobject coordinates as the biochemical data. In this case, a 3D surfaceregistration can be applied in order to superimpose, merge, or fuse themorphological data and the biochemical data. As an alternative to 3Dobject coordinates, anatomic landmarks can be used to register themorphological data and subsequently the biochemical data in a 3D objectcoordinate system. 3D object coordinate systems can then be matched bymatching the landmarks obtained from the morphological data with thoseobtained from the biochemical data.

Thus, another aspect of this invention is a method for assessing thecondition of a subject's cartilage in a joint, the method comprisesobtaining a three dimensional biochemical representation of thecartilage, obtaining a morphological representation of the cartilage,and merging the two representations, and simultaneously displaying themerged representations on a medium. The merged representations are thenused to assess the condition of a cartilage, estimate the loss ofcartilage in a joint, determining the volume of cartilage loss in aregion of cartilage defect, or estimating the change of a region ofcartilage at a particular point in time or over a period of time. Onecan see that similar steps would be followed as spelled out for the useof a thickness map or biochemical map.

Simultaneous display of morphological data with biochemical dataprovides a useful tool to assess longitudinal changes in morphology orarticular cartilage and biochemical composition of articular cartilage,for example during treatment with chondroprotective andchondroregenerative agents.

Part of the unique aspect of this technology is that it lends itself toassessment of a patient from a distant position after an image is takenof the joint under evaluation. Thus one aspect of this invention is amethod for assessing the condition of cartilage in a joint from adistant location. The method comprises

(a) electronically transferring an electronically-generated image of acartilage of the joint from a transferring device to a receiving devicelocated distant from the transferring device;

(b) receiving the transferred image at the distant location;

(c) converting the transferred image to a degeneration pattern of thecartilage; and

(d) transmitting the degeneration pattern to a site for analysis.

The degeneration pattern includes a measure of cartilage thickness orregional cartilage volume.

The electronically generated image of the cartilage preferably is an MRimage and the degeneration pattern can be displayed as athree-dimensional image as a thickness pattern, a biochemical contentpattern or a merged thickness biochemical pattern. The electronicallygenerated image is transmitted via Dicom, using the internationalstandards for transmission of such images.

Another aspect of the invention is a kit for aiding in assessing thecondition of cartilage in a joint of a mammal, which kit comprises asoftware program, which that when installed and executed on a computerreads a cartilage degeneration pattern presented in a standard graphicsformat and produces a computer readout showing a cartilage thickness mapof the degenerated cartilage.

The software can be installed in a PC, a Silicon Graphics, Inc. (SGI)computer or a Macintosh computer. Preferably, the software calculatesthe thickness or regional volume of a region of degeneration of thecartilage which does not include the entire volume of the articularcartilage.

The Movement Pattern

To acquire a movement pattern of a joint in accordance with thisinvention, one obtains an internal image of the bones in a joint,preferably using MRI techniques, and obtains an external image of thebones in motion. The images are correlated, preferably through the useof external marker sets, to give a pattern that shows a static or movingcondition. The correlated images are then displayed and the relationbetween the movement and degeneration patterns is determined.

Obtaining an Internal Image of Joint with Bones

To obtain an internal image of a joint with the associated bones, onepreferably uses MRI techniques that provide an image of the bones oneither side of the joint. Here, it is important to use the imagingtechnique that gives the best image of the bones and how they interact.Because the internal image of the bones can be combined with the imageof the bones obtained by external measurements, it is particularlyuseful, and therefore preferred, to use external reference markers thatcan be similarly-positioned to the markers used in obtaining theexternal measurements. The external markers can be placed at anylandmarks about the joint of interest. At least three markers are usedfor each limb being imaged. Preferably the markers will be made of amaterial that not only will be detected by MRI imaging techniques, butalso will be detected by external imaging techniques. The markers willbe associated with a means to affix them to the skin and preferably havean adhesive portion for adhering to the skin and a detectable entitythat will show up on the MRI image.

The preferred MRI imaging technique useful for obtaining an internalimage is a spoiled 3D gradient echo, a water selective 3D gradient echoor a 3D DEFT sequence. A further discussion may be found hereinbefore orin the 2^(nd) Edition of Brown and Semelka's book entitled “MRI BasicPrinciples and Applications.”

Once an MRI image is obtained the image is manipulated to enhance theimage of the bones. Procedures similar to those discussed hereinbeforefor cartilage may be used, but modified for application to bone images.

Creating Three-Dimensional (3D) Image of Joint/Bones

Three-Dimensional Geometric Model Generation

After the 3D image of a joint with bones, the set of segmented twodimensional MR images can be transformed to a voxel representationinside AVS Express (Advanced Visual Systems, Inc., Waltham, Mass.).Every voxel has a value of zero if it is not within an object ofinterest or a value ranging from one to 4095, depending on the signalintensity as recorded by the 1.5 T MR. An isosurface can then becalculated that corresponds to the boundary elements of the region ofinterest. A tesselation of this isosurface can be calculated, along withthe outward pointing normal of each polygon of the tesselation. Thesepolygons can then be written to a file in a standard graphics format(Virtual Reality Modeling Language Version 1.0).

As discussed hereinbefore, the use of reference markers on the skinaround the joint and the bones can provide an image that can later bematched to the reference markers for the cartilage image and the boneimages obtained from external measurements.

Alternatively, a semi-automated, 3D surface-based registration techniquethat does not require the use of an external frame or fiducial markerscan be used. This 3D surface-based registration technique can be used tomatch the anatomic orientation of a skeletal structure on a baseline anda follow-up CT or MRI scan. We extended a robust and accurate 2D edgedetector (Wang-Binford) to 3D. This detector is described hereinbefore.

A registration technique for the femoral condyles and the tibial plateauis shown in FIG. 10. It shows an example where 3D surfaces of thefemoral condyles were extracted from two differently orientedT1-weighted spin-echo MRI scans (baseline A and follow-up B,respectively) obtained in the same patient in neutral position (A) andin 40 degree external rotation (B). The 3D surfaces were used to derivea coordinate transformation relating the two scans. FIG. 10Cdemonstrates the use of the derived transformation to re-register scan Bin the object coordinate system of scan A. Such a transformationrelating two T1-weighted scans can then be used to register DEFTcartilage-sensitive scans that are acquired in the same respectiveorientations as the A and B T1-weighted scans.

We performed the registration using a Sun Sparc 20 workstation with 128MBytes of memory. The surface detection algorithm extractedapproximately 12,000 surface patches from each data set. The surfaceextraction and registration routines took about 1 hour in total.

Since the algorithm for 3D surface registration of the femoral condylesalso computes the surface normals for the medial and lateral femoralcondyles on a pixel-by pixel basis, it can form the basis for developingmaps of cartilage thickness. FIG. 11 shows an example of a 2D map ofcartilage thickness derived from the surface normals of the lateralfemoral condyle. FIG. 11A shows a proton density fast spin-echo MR imagethat demonstrates a focal cartilage defect in the posterior lateralfemoral condyle (black arrows). White arrows indicate endpoints ofthickness map. FIG. 11B is a 2D cartilage thickness map thatdemonstrates abrupt decrease in cartilage thickness in the area of thedefect (arrows). The A thickness between neighboring pixels can be usedto define the borders of the cartilage defect. Note diffuse cartilagethinning in area enclosed by the astericks (*).

In another embodiment, cartilage sensitive images can be used instead ofT1-weighted or T2-weighted scans and the surface match can be performedbased on the cartilage contour.

Alternatively, anatomic landmarks present on both baseline and follow-upscans can be used to match the data obtained during the baseline andthose obtained during the follow-up scan. Another alternative formatching the baseline and the follow-up scan includes the use ofexternal or internal fiducial markers that can been detected with MRimaging. In that case, a transformation is performed that matches theposition of the markers on the follow-up scan with the position of themarkers on the baseline scan or vice versa.

Obtaining an External Image of Joint/Bones

Before merging or superimposing morphological maps of articularcartilage obtained by MR imaging with biomechanical data, one mustobtain the biomechanical data. Such biomechanical data include, but arenot limited to, estimations of static loading alignment in standing orweight-bearing position and lying or non-weight-bearing position, aswell as during joint motion, e.g., the movement of load-bearing pathwayon the cartilage in the knee joint during gait. Biomechanical data maybe generated using theoretical computations, based on data stored in adatabase that can be accessed by calling up and screening for certaincharacteristics. Alternatively, gait analysis may be performed for anindividual and data obtained during gait analysis may be merged or fusedwith morphological MRI data. Morphological data and biomechanical datacan then be easily displayed simultaneously using different colors,opacities, and or gray scales. Additionally, the load-bearing pathway,for example around a cartilage defect, can be plotted or superimposedonto morphological maps.

Preferably, reference markers or fiducial markers can be applied to theexternal surface on the skin overlying the joint. These markers adhereto the skin are typically made of materials that can be detected withMRI and that can be used to register joint motion during biomechanicalanalysis, e.g. gait analysis. These markers can then be used tocorrelate the morphological with the biomechanical data.

Simultaneous display of morphological data with biomechanical dataprovides a useful tool to assess the load pathway applied to articularcartilage and inside and around cartilage defects. Estimation of loadpathway applied in and around a cartilage defect can be used to assess acartilage defect and to guide the choice of therapy, e.g. treatment withchondroprotective or chondroregenerative agents, osteochondralallografting, cartilage transplantation, femoral or tibial osteotomy, orjoint replacement surgery.

Recording Static Joint/Bones and Joint/Bones in Movement

In obtaining an external image of the bones on either side of a joint,one must record a static image as well as a moving image of the subjectjoint and bones. For analysis of the knee joint, gait analysistechniques have been shown to be very effective in generating accurate,reproducible data on the six degree of freedom motion of the knee. Themotion of the knee joint can be quantified in terms of flexion, rotationand displacement. Fidelity in the dynamic visualizations of subjectspecific MR generated knee geometry and subsequent contact surfacedetermination call for a high degree of accuracy for the motion captureportion of the studies.

Gait Analysis Activities

In performing a gait analysis, a subject is tested standing still,laying down, walking or running on a level surface, flexing a leg in astanding position, ascending and descending stairs, flexing the leg in aseated position, and the like. The level walking measurements caninclude, but is not limited to, six stride cycles for each side over arange of walking speeds. The subject can be instructed to walk at acomfortable speed (normal), slower than normal and faster than normal.Typically, this protocol produces gait measurements over a range ofwalking speeds. The standing and laying portions of the protocol can beused in the cross registration to the MR data. The instrumentationpreferably includes, at least a two camera, video-based opto-electronicsystem for 3-D motion analysis, a multi-component force plate formeasurement of foot-ground reaction force and a computer system foracquisition, processing and analysis of data.

Anatomic Coordinate Systems

Currently, the anatomic coordinate systems are defined through bonylandmarks which can be identified through palpation. To describe themotion of the underlying bones in terms of the global coordinate systema subset of the markers in a point cluster technique (discussedhereinafter) are referenced to bony landmarks on the femur and tibia.Techniques described previously by Hopenfeld and Benedetti can be usedto locate these bony landmarks. The anatomic coordinate systems used canbe similar to that previously described by LaFortune with the exceptionof the origin of the femoral coordinate system. For the thigh segment, acoordinate system is located in the femoral condyles. The femoralcondyles medial(M)-lateral(L) axis (FIG. 12) runs through thetrans-epicondylar line (a line drawn between the medial-lateral femoralepicondyles). The midpoint of this axis is the origin. Theinferior(I)-superior(S) axis runs parallel to the long axis of thefemur, passing through the midpoint of the trans-epicondylar line. Theanterior(A)-posterior(P) axis is the cross product of the medial-lateraland inferior-superior axes. The final position of the inferior-superioraxis is made orthogonal to the anterior-posterior and medial-lateralaxis through a cross product operation (FIG. 13). For the shank segment,the tibial coordinate system begins with the medial-lateral axis runningthrough the most medial and lateral edges of the plateau. Theinferior-superior axis is perpendicular to the medial-lateral axispassing through the tibial eminence. The anterior-posterior axis is thecross product of the medial-lateral and inferior-superior axes.

Placement of Markers Prior to Activity

In assessing a joint, the lower extremity can be idealized as 3 segmentswith six degree-of-freedom joints at the knee and ankle. For the mobileactivities described above, at least 3 markers per segment are used.FIG. 14 shows 21 passive retro-reflective markers located on the leg:some at bony prominences (greater trochanter, lateral malleolus, lateralepicondyle, lateral tibial plateau), some clustered on the thigh andshank (Fa1-3, 11-3, Fp1-3 Ta 1-3, T11-13). Additionally, two markers areplaced on the foot at the lateral aspect of the calcaneus and base ofthe fifth metatarsal and one on the pelvis at theiliac crest). Duringthe static activities (standing still, laying down) 7 additional markersare placed: medial malleolus, medial epicondyle, medial tibial plateau,medial and lateral superior patella, medial and lateral inferiorpatella. The eight markers nearest to the knee joint can be filled withGadolinium, and can be replaced at these same locations prior to the MRimages (FIG. 15). The locations can be marked with a non-toxicmarker-pen.

Reference Database

The reference database is typically a compendium of demographic andmotion analysis data for all subjects whose data has been processed by acentral processing site. This database can contain fields describingeach of the subject's name, age, height, weight, injury types,orthopedic medical history, other anatomic measurements (thigh length,shank length, shoe size, etc.). The database can also contain theresults of any and all gait analysis run on these patients. This caninclude, for all activities tested (walk, run, jog, etc.), a number ofpeak valves (peak knee flexing, peak hip adduction movement; toe-out,angle, etc). along with the motion trajectories of the limb segmentswhile the subjects are performing different activities.

In order to obtain a typical motion profile, the sex, age, height,weight, limb length, and type of activity desired can be entered as anaverage into the database. The database searches for a set of subjectsmost closely watching the input average. From this set of data, atypical motion pattern is distilled and a data set is output. This dataset can include, over a time interval, the motion characteristics:hip/knee/ankle/flexion/extension angles, knee/hip/ankleadduction/abduction angles, movement, stride length, cadence, etc. Thisdata can then be used to drive an animation of the motion of the desiredjoint.

Process Image of Joint/Bones

Calculation of Limb Segment Parameters

Each limb segment (thigh, shank and foot) can idealized as a rigid bodywith a local coordinate system defined to coincide with a set ofanatomical axes (the assumption of rigidity is dropped in calculatingthe location of the femur and tibia). The intersegmental moments andforces can be calculated from the estimated position of the bones, theground reaction force measurements, and the limb segment mass/inertiaproperties. The moment at the knee can be resolved into a coordinatesystem fixed in a tibial reference system with axes definingflexion-extension, abduction-adduction, and internal-external rotation.

This approach provides results in a range of patients in a highlyreproducible manner. Typically the magnitudes of the moments aredependent on walking speed. To control for the influence of walkingspeed, the walking speed closest to 1 meter/second is used. This speedis within the normal range for the type of patients for which theinvention is particularly useful. In addition to the gait trialcollected at 1 meter/second, self-selected speeds can also be evaluatedto give a good correlation between gait-quantitative estimates of jointload lines and other measures when using self-selected speeds. In orderto test patients under their typical daily conditions, medicationsshould not be modified prior to gait analyses.

Point Cluster Technique

The Point Cluster Technique (PCT) movement analysis protocol is anextensible and accurate approach to bone motion estimation. Basically, anumber of retro-reflective markers (e.g. retro-reflective material from3M, Corp.) are attached to each limb segment under observation. Multiplevideo cameras can acquire data with the subject standing still andduring activities of interest. An over-abundance of markers on each limbsegment is used to define a cluster coordinate system, which is tied toan anatomically relevant coordinate system calculated with the subjectat rest.

The standard PCT transformations are described below. In short, eachmarker is assigned a unit mass and the inertia tensor, center of mass,principal axes and principal moments of inertia are calculated. Bytreating the center of mass and principal axes as a transformation,local coordinates are calculated. Another set of coordinate systems isestablished; limb segment specific anatomic landmarks are identifiedthrough palpation and a clinically relevant coordinate system defined.For the femur and tibia, these anatomic coordinate systems are shown inFIG. 12. The transformation from the reference cluster coordinate systemto the anatomic coordinate system is determined with the subject at restby vector operations. During an activity, the transformation from theglobal coordinate system to the cluster coordinate system is calculatedat each time step. To place the anatomic coordinate in the global systemduring the activity, the reference coordinate system to anatomic systemtransformation is applied, followed by the inverse global coordinatesystem to cluster coordinate system transformation for each time step.

In the Point Cluster Technique (PCT) a cluster of N markers can beplaced on a limb segment of the subject. The location vector of eachmarker in the laboratory coordinate system is denoted as G(i,t) formarker i, (i=1, 2, . . . , N) at time t, t_(o)−t≦t_(f). A unit weightfactor is assigned to each marker for the purpose of calculating thecenter of mass, inertia tensor, principal axes and principal moments ofinertia of the cluster of markers. The cluster center of mass andprincipal axes form an orthogonal coordinate system described as thecluster system. The local coordinates of each of the markers relative tothis coordinate system are calculated. ThenG(i,t)=C(t)+E(t)·L(i,t)=T _(c)(t)·L(i,t)i=1 . . . Nwhere G(t) is a matrix of all marker coordinate vectors, C(t) is thecenter of mass of G(t), E(t) is the matrix of eigenvectors of theinertia tensor of G(t), and L(i,t) are the local coordinates of markeri.

These markers are observed by opto-electronic means while the subjectperforms activities and while standing completely still in a referenceposition. With the subject in this same reference position, a subset ofthe markers is observed relative to the underlying bones by othertechniques, which might include x-rays, CT scan, or palpation.

The measured marker locations are defined with respect to theunobservable location and orientation of the bone byG(i,t)=P(t)+O(t)·R(i,t)=T _(b)(t)·R(i,t)i=1 . . . Nwhere P(t) is the location and O(t) is the orientation of a coordinatesystem embedded in the bone and R(i,t), also unobservable, are thetrajectories of the markers relative to the underlying rigid bodycoordinate system at time t. The bone and cluster systems are eachorthogonal systems, related by the rigid body transformation T_(bc) (t):L(i,t)=T _(bc)(t)·R(i,t)

substituting and eliminating R(i,t) yieldsT _(b)(t)=T _(c)(t)*T _(cb)(t)

To maintain physical consistency, T_(cb)(t)=T_(bc)(t)⁻¹ must be theinertia tensor eigendecomposition transformation of R(i,t). Once R(i,t)are specified, T_(cb)(t) and subsequently T_(b)(t) are calculable.

Point Cluster to Anatomic Coordinate System Transformation

From these equations one can also relate the global coordinate systemwith respect to a limb segment system. As an example of how thesesystems can be used to describe joint motion, one can consider thetibio-femoral joint. The motion that is of interest is how the femoralcondyles move with respect to the tibial plateau. This is done by firstdefining a set of coordinate axes in the femoral condyles and the tibialplateau.

A coordinate system is located in both the femoral condyles and thetibial plateau. The femoral condyles medial-lateral (ML) axis runsthrough the trans-epicondylar line (TEL), a line drawn between the MLfemoral epicondyles. The midpoint of this axis is the origin. Theinferior-superior (IS) runs parallel to the long axis of the femur,passing through the midpoint of the TEL. The anterior-posterior (AP) isthe cross product of the ML and IS axes. The tibial coordinate systembegins with the ML axis running through the most medial and lateraledges of the plateau. The IS axis is perpendicular to the ML axispassing through the tibial eminence. The AP axis is the cross product ofthe ML and IS axes. These are known as the anatomic coordinate system(A(t)^(thigh), A(t)_(shank)).

Relating the cluster system to the anatomic coordinate system is done byuse of another transformation matrix. This is done by relating the thighcluster to a cluster of markers, a sub cluster, that is related to thefemoral condyles and femur (cluster to anatomic transformation).R(t)_(thigh) =U(t)_(high) A(t)_(thigh)

The tibia has a similar transformation matrix.R(t)_(shank) =U(t)_(shank) A(t)_(shank)

Therefore, from a cluster of markers in the global system, motion of thefemur with respect to the tibia can be determined by:TS(t)=A(t)_(thigh) ·G(t)_(thigh) ·R(t)_(shank) ·A(t)_(shank)

Here TS(t) is the motion of the thigh with respect to the shank.

Angles are calculated by a projection angle system, an axis from thefemoral anatomic system and one from the tibia are projected onto aplane in the tibial coordinate system. For example, flexion/extensioncan be determined by projecting the IS axis of the femur and tibia ontothe sagittal plane (AP-IS plane) of the tibia.

Validation of the Point Cluster Technique

The point cluster technique was evaluated as a method for measuring invivo limb segment movement from skin placed marker clusters. An Ilizarovdevice is an external fixture where 5 mm diameter pins are placeddirectly into the bone on either side of a bony defect. The rigidexternal struts affixed to these pins form a rigid system fixed in theunderlying bone. Two subjects were tested with Ilizarov fixationdevices. One subject had the Ilizarov device placed on the femur andsecond subject had the device placed on the tibia. Each subject wasinstrumented with point clusters placed on the thigh and shank segment.In addition, markers were placed on the Ilizarov device to establish asystem fixed in the underlying bone.

The relative angular movement and translational displacement between thesystem affixed in the bone and the point cluster coordinate system werecalculated while ascending a 20-cm step (Step Test). Angular changesbetween the three orthogonal axes fixed in the bone versus three axes inthe point cluster were calculated. The average difference over thetrials for three axes were 0.95∓1.26, 2.33±1.63, and 0.58±0.58 degrees.Similarly, the average error for the distance between coordinate systemswas 0.28±0.14 cm. The second subject with the Ilizarov device placed onthe femur could not perform the Step-Test, but was able to perform aweight-bearing flexion test where his knee flexed to approximately 20°from a standing position. The average change between the coordinateorigin was 0.28±0.14 cm. The changes in axis orientation were 1.92±0.42,1.11±0.69 and 1.24±0.16 degrees.

The simultaneously acquired motion for a coordinate system embedded inbone (Ilizarov system) and a set of skin-based markers was compared. Atevery time instant the location and orientation of the Ilizarov system,the rigid body model skin marker system, and the interval deformationtechnique skin marker system were determined. The change in thetransformation from the Ilizarov system to one of the skin markersystems over time is a measure of the deformation unaccounted for in theskin marker system.

The interval deformation technique produced a substantial improvement inthe estimate of the location and orientation of the underlying bone. Forperfectly modeled motion there would be no relative motion between theIlizarov system and the skin marker system over the time interval. Thechange in the transformation from the Ilizarov system to the skin markersystems are shown in FIGS. 14 and 15, for location and orientationrespectively, for both a rigid body model and the interval deformationtechnique. For this single data set, the location error was reduced from7.1 cm to 2.3 cm and the orientation error from 107 degrees to 24degrees, with the error summed over the entire time interval. Thesubject performed a 10 cm step-up; the marker deformation was modeled asa single Gaussian function.

Deformation Correction

There are a number of algorithmic alternatives available to minimize theeffects of skin motion, soft tissue deformation, or muscle activationthat deform the externally applied markers relative to the underlyingbone. The Point Cluster Technique decreases the effects of markermovement relative to the underlying bone through averaging. If morecorrection is required, one of a number of deformation correctiontechniques may be added. In order of increasing computational complexityand deformation correction ability, these are rigid body linear leastsquare error correction, global optimization correction, anatomicartifact correlation correction and interval deformation correction.

An overview of the Interval Deformation Correction Technique is givenbelow. In short, the technique provides a maximum likelihood estimate ofthe bone pose, assuming that each marker on a limb segment deformsrelative to the underlying bone in some functional form. The techniqueparameterizes these functional forms and then performs a multi-objectivenon-linear optimization with constraints to calculate these parameters.This is an extremely computationally intensive technique, with thecurrent instantiation of the algorithm requiring 6-8 hours per limbsegment of running time on 266 MHz Pentium 2 computer.

Interval Deformation Technique

Since Tc can be calculated directly from the global coordinates of themarkers, the remainder of this development only examines thedetermination of R(i,t) and subsequently T_(cb)(t). For this reducedproblem, the input data is the local coordinates in the cluster systemL(i,t) for all i, T_(o)≦t≦t_(f). It can be assumed that each marker hassome parameterized trajectory, d(a_(ij), t), relative to the underlyingbone at each time step, with independent and identically distributednoises v(i,j,t)R _(j)(i,t)=d(a _(ij) ,t)+v(ij,t)j=1 . . . 3 i=1 . . . Nor, equivalentlyR(i,t)=F(a _(i) ,t)+v(i,t)i=1 . . . Nwhere a_(ij) is a vector of parameters for marker i, ordinate j; a_(i)is a vector of parameters combining all of the parameters for all of theordinates of marker i. Then the estimate of the data, M(i,t), can begiven byM(i,t)=T _(bc)(t)·R(i,t)

Without further restrictions the problem is indeterminate, as thelocations of the markers in the bone system R(i,t) are never observablewith the opto-electronic system. The indeterminate problem can beconverted to a chi-squared estimate problem through a series of steps.An observation of the truly unobservables at the time boundaries isinferred; that is, it is assumed that T_(cb)(t≦t_(o)) andT_(cb)(t≧t_(f)) are observed. The value of T_(cb) can be selecteddepending on the activity being studied. For example, consider the stepup activity, where the subject starts and stops in the referenceposition. For this activity the body is not deforming outside theestimation interval; that is, the markers are not moving with respect tothe bone:T _(cb)(t<t _(o))=T _(cb)(t=t _(o)) and T _(cb)(t>t _(f))=T _(cb)(t_(f))

It can now be assumed that the noise functions v(i, j, t) are normaldistributions with individual standard deviations σ(i, j, t), theprobability P(ij,t) of the data for ordinate j, marker i, time t being arealization of the stochastic process is given by:${P( {i,j,t} )} \propto {\exp( {{- \frac{1}{2}}( \frac{{L( {i,j,t} )} - {M( {i,j,t} )}}{\sigma( {i,j,t} )} )^{2}} )}$

Provided the noise functions v(i, j, t) are independent of each other,the probability of the entire data set being a realization is a productof each of the individual probabilities:${P( {i,j,t} )} \propto {\prod\limits_{i = t}^{N}\quad{\prod\limits_{j = t}^{3}\quad{\prod\limits_{t = t_{0}}^{f_{i}}\quad{\exp( {{- \frac{1}{2}}( \frac{{L( {i,j,t} )} - {M( {i,j,t} )}}{\sigma( {i,j,t} )} )^{2}} )}}}}$

Maximizing this probability can be equivalent to minimizing the negativeof its logarithm, yielding the familiar chi-square criteria. As anintermediate step the following error matrices can be defined:${{X( {a,t} )} \ni {X( {a,t} )}_{i,j}} = {( \frac{{L( {i,\quad j,\quad t} )} - {M( {i,\quad j,\quad t} )}}{\sigma( {i,\quad j,\quad t} )} )^{2}\begin{matrix}{i = {1\quad\ldots\quad N}} & {j = {1\quad\ldots\quad 3}}\end{matrix}}$and seek a which in some sense minimizes X(a), a matrix whose elementsrepresent the error over the entire time interval for each ordinate ofeach marker. If the normal noise distribution assumption is true, thenthis minimization results in the maximum likelihood estimate of theparameterization, and by inference maximum likelihood estimate of thetransformation from the bone system to the cluster system. If the normalnoise assumption is not true, the chi-squared estimate is stillappropriate for parameter estimation; the results cannot be interpretedas a maximum likelihood estimate, but, for example, confidence regionson the estimate or the formal covariance matrix of the fit can bedetermined.

Obtaining the parameter set a is a computationally complex operation.The approach taken was to define a scalar to represent this entire errormatrix,${X(a)} = {\sum\limits_{t = t_{0}}^{t_{f}}{X( {a,t} )}}$and seek a that minimizes f(a).

The limits on marker motion previously discussed can now be convertedinto deformation constraints, which allow the formulation of the problemas a general non-linear programming problem. The constraints arise fromtwo sources; human limb segments do not deform outside a small range,and the locations of the markers are chosen with specific properties inmind. For computational purposes, the deformation constraints areselected to be:

1. The axes of the cluster system moves by less than 15 degrees relativeto the bone system.

2. The center of mass of the cluster system moves by less than 3 cmrelative to the bone system.

3. The markers move by less than 4 cm relative to the bone system.

4. Each of the principal moments of inertia of the cluster system changeby less than 25 percent from the reference values.

The Point Cluster Technique marker set was designed to ensure that thecluster of points is non-coplanar and possess no axes of rotationalsymmetry. These properties ensure a local coordinate system that is welldefined and unambiguous over the entire time interval. The constraintsare then:

5. The ratio of the smallest principal moment of inertia of the clustersystem to the largest is more than 5 percent; the magnitude of thesmallest principal moment of inertia of the cluster system is greaterthan some small positive value.

6. The principal moments of each axis are different from each other byat least 5 percent.

The general problem can then be formulated:Minimize f(a)aεR^(D)Subject to:g _(i)(a)=0 i=1 . . . m _(e)g _(i)(a)□0 i=m _(e)+1 . . . Ma₁≦a≦a≦a_(u)where D is the total number of parameters; m_(e), the number of equalityconstraints, is 0; and m, the total number of constraints, is 10.

The approach taken to verify the operation of the algorithmimplementation began with generating a set of 50 synthetic data setswith known characteristics. The program was then applied to all of thedata sets. The program results were then compared to the known,generated deformation. Error results were calculated for both theinterval deformation technique descried herein and for the standardrigid body model formulation.

The 50 trial data sets were processed through the algorithm. The resultsover all of the trial sets are summarized in Table I, where the centerof mass and direction cosine error of the interval deformation techniqueand the rigid body model are compared. After processing by the intervaldeformation algorithm the center of mass error has been reduced to 29%and the direction cosine error has been reduced to 19% of the rigid bodymodel error. In a t-test for paired samples, both of these decreaseswere significant at p<0.001.

Validation of the Interval Deformation Correction Technique

A subject fitted with an Ilizarov external fixation was observed withthe optoelectronic system. The Point Cluster Marker set was affixed tothe subject's shank (6 markers), along with a set of four markersrigidly attached to the Ilizarov device, which is rigidly connected tothe tibia with bone pins. These four markers define a true bone embeddedcoordinate system. Data were acquired by GaitLink software (ComputerizedFunctional Testing Corporation) controlling four Qualisys camerasoperating at a video frequency of 120 Hz. Three dimensional coordinateswere calculated using the modified direct linear transform.

The subject was a 46 year old male (height 1.75 m, weight 84.1 kg)fitted with a tibial Ilizarov external fixation device. The device wasrigidly attached to the tibia with nine bone pins, located in three sets(top, middle, and bottom) of three (medial, anterior, and lateral). Theclinical purpose of the device was tibial lengthening; the test on thesubject was performed two days prior to final removal of the device. Thesubject exhibited a limited range of motion and was tested performing a10 cm step-up onto a platform.

The simultaneously acquired motion for a coordinate system embedded inbone (Ilizarov system) and a set of skin-based markers was compared. Atevery time instant the location and orientation of the Ilizarov system,the rigid body model skin marker system, and the interval deformationtechnique skin marker system was determined. The change in thetransformation from the Ilizarov system to one of the skin markersystems over. time is a measure of the deformation unaccounted for inthe skin marker system.

The interval deformation technique produced a substantial improvement inthe estimate of the location and orientation of the underlying bone. Forperfectly modeled motion there would be no relative motion between theIlizarov system and the skin marker system over the time interval. Thechange in the transformation from the Ilizarov system to the skin markersystems are shown in FIGS. 14 and 15 for location and orientationrespectively, for both a rigid body model and the interval deformationtechnique. For this single data set, the location error was reduced from7.1 cm to 2.3 cm and the orientation error from 107 degrees to 24degrees, with the error summed over the entire time interval. Thesubject performed a 10 cm step-up; the marker deformation was modeled asa single Gaussian function.

Correlating Results from Gait Analysis and Geometrical Representationsof the Bone

In correlating the load pattern obtained from a gait analysis using,e.g. the PCT, with the geometrical representation of the bone from thesegmented MRI data, one can be guided by the general process asdescribed below. The process allows for dynamic visualization (i.e.animations) of high-resolution geometrical representations derived fromMRI scans (or other imaging techniques). The motion of the subjectspecific anatomic elements is generally driven by data acquired from themotion (gait) lab. Fidelity of these animations requires calculation andapplication of a sequence of rigid body transformations, some of whichare directly calculable and some of which are the result ofoptimizations (the correction for skin marker deformation from rigiditydoes not use the rigid body assumption, but generates a correction thatis applied as a rigid body transform).

The process comprises:

a) acquiring data from MRI (or other imaging techniques), and PCT gaitprotocols;

b) directly calculating a set of transformations from the data;

c) calculating a set of transformations from optimizations, as needed;

d) generating a 3D geometric representation of the anatomic element fromthe MR data; and

e) applying the transformations of (b) and (c) to the 3D geometricrepresentation.

Each of these steps are described in detail below.

Acquiring the Data from MRI (or other Imaging Techniques) and PCT GaitProtocols

In the Point Cluster Technique (PCT) protocol, a patient can have anumber of retro-reflective markers attached to each limb segment underobservation. Multiple video cameras acquire data with the subjectstanding still and during activities of interest.

In addition, in order to correspond activities in the gait lab with theMRI scans, another reference data set (subject standing still,prescribed posture) can be acquired using 8 additional markers clusteredabout the knee. These markers are filled with gadolinium-DTPA andcovered with a retro-reflective material to allow for correlationbetween the MRI image and the video data.

Directly Calculating a Set of Transformations from the Data

The transformations are described in detail in [Andriacchi T P,Alexander E J, Toney M K, Dyrby C O, Sum J. J Biomech Eng 1998; 120(12):743-749]. In short, each marker can be assigned a unit mass and theinertia tensor, center of mass, principal axes and principal moments ofinertia can be calculated. By treating the center of mass and principalaxes as a transformation, local coordinates arcan be e calculated.Another set of coordinate systems can also be required for thistechnique; limb segment specific anatomic landmarks can be identifiedthrough palpation and a clinically relevant coordinate system can bedefined. The required transformations are summarized in Table 1 below.

Calculating a Set of Transformations from Optimizations

There are three required transformations:

Optimization 1. One can calculate the linear least square error rigidbody transformation from the MRI common local coordinate system to theVID common local coordinate system.

Optimization 2. For each limb segment, one can calculate the linearleast square rigid body transformation from the MRI limb segmentanatomic coordinate system to the video limb segment anatomic coordinatesystem (obtained from the gait analysis), using a subset of commonmarkers appropriate for each segment.

Optimization 3. One can calculate a correction for the deviation of thelimb segment from rigidity during each time step of the activity, usingthe PCT with either the mass redistribution [Andriacchi T P, Alexander EJ, Toney M K, Dyrby C O, Sum J. J Biomech Eng 1998; 120(12): 743-749] orinterval deformation algorithms [Alexander E J, Andriacchi T P:Correcting for deformation in skin-based marker systems. Proceedings ofthe 3rd Annual Gait and Clinical Movement Analysis Meeting, San Diego,Calif., 1998].

Generating a 3D Geometric Representation of the Anatomic Element fromthe MR Data

The MR slices are segmented for the multiple anatomic and fiducialelements. The slices are combined to a voxel representation. Anisosurface can be calculated from the boundary voxel elements. Atessellation of the isosurface can be calculated, along with the outwardpointing normal for each surface element. This data can then be storedin a standard 3D graphic format, the Virtual Reality Modeling Language(VRML).

Appling the Transformation Sequence to the Geometric Representation

The transformation sequence is provided below in Table 1. Thistransformation sequence can be applied to each of the anatomic elementsover each time step of the activity, starting with sequence 6. TABLE 1SEQ FROM SYSTEM TO SYSTEM X_(FORM) 1 MR Global MR Local ED1 2 MR LocalCommon Local OPT

3 Common Local MR Anatomic ANA2 4 MR Anatomic VID Anatomic OPT2 5 VIDAnatomic VD Ref ANA3 6 VID Ref VID Deformed(t) ED3 7 VID Deformed(t) VIDBone(t) OPT3 8 VID Bone(t) VD Global(t) ED4Correlating Marker Sets

As pointed out at numerous places in the specification, the use ofexternal reference markers that are detectable by both MRI and opticaltechniques can be an important and useful tool in the method of thisinvention. The use of the reference markers can form the basis for anaspect of this invention that is a method for correlating cartilageimage data, bone image data, and/or opto-electrical image data for theassessment of the condition of a joint of a human. This methodcomprises, obtaining the cartilage image data of the joint with a set ofskin reference markers placed externally near the joint, obtaining thebone image data of the joint with a set of skin reference markers placedexternally near the joint, obtaining the external bone image dataopto-eletrical image data of the joint with a set of skin referencemarkers placed externally near the joint. Using the skin referencemarkers, one can then correlate the cartilage image, bone image andopto-electrical image with each other, due to the fact that each skinreference marker is detectable in the cartilage, bone andopto-electrical data. The cartilage image data and the bone image datacan be obtained by magnetic resonance imaging, positron emissiontomography, single photon emission computed tomography, ultrasound,computed tomography or X-ray. Typically, MRI will be preferred. In thecase of X-ray, further manipulations must be performed in which multipleX-ray images are assimilated by a computer into a 2 dimensionalcross-sectional image called a Computed Tomography (CT) Scan. Theopto-electrical image data can be obtained by any means, for example, avideo camera or a movie camera. Multiple skin reference markers can beplaced on one or more limbs of the patient prior to imaging. The skinreference markers are described hereinbefore.

By a sequence of calculations a set of transformations that will takethe subject specific geometric representation of anatomic elementsdetermined from the MR image set to the optical reference coordinatesystem. From the optical reference coordinate system, the standard PointCluster Technique transformation sequence is applied to generate dynamicvisualizations of these anatomic elements during activities previouslyrecorded in the motion lab. Fidelity of these dynamic visualizations(and subsequent contact surface determination) requires the calculationand application of a sequence of rigid body transformations. Some ofthese are directly calculable and some are the result of optimizations(the correction for skin marker deformation from rigidity does not usethe rigid body assumption, but generates a correction that is applied asa rigid body transform).

The first required transformation can be from the MR global coordinatesystem to the MR center of mass/principal axis coordinate system. Thiscan be done by calculating the center of mass of each of the individualmarkers, resulting in a set of eight three dimensional points. Each ofthese points can be assigned a unit mass, and the center of mass,inertia tensor, and principal axes can be calculated. The same procedurecan be performed on these markers as determined by the optical system,providing a transformation from the optical global system to a center ofmass/principal axis system.

If the relative orientation of the tibia and femur as determined by theMR system and the optical system are identical, it is only necessary toapply the optical reference system to the anatomic system transformationof the MR local data. If this is not the case, an optimizationcalculation can be performed to determine the rotation and translationof, for example, the femur with respect to the tibia. One then cancalculate the linear least square rigid body transformation from the MRlimb segment anatomic coordinate system to the video limb segmentanatomic coordinate system prior to applying the Point ClusterTransformations.

For visualization or contact surface determination, one can examine therelative motion of one segment to the other, for example the motion ofthe femur relative to a fixed tibial frame. This can be accomplished byapplying the global to tibial anatomic system transform to all of theelements. An example of this type of visualization is given in FIG. 18.The Figure shows what can be referred to as functional joint imaging.FIG. 18A is a photograph demonstrating the position of the externalmarkers positioned around the knee joint. The markers are filled withdilute Gd-solution. B is Sagittal 3D SPGR image through the medialfemorotibial compartment. Two of the external markers are seenanteriorly as rounded structures with high signal intensity. C is 3Dreconstruction of femoral and tibial bones (light grey), externalmarkers (dark grey), femoral cartilage (red), and tibial cartilage(blue) based on the original SPGR MR images. D-I show a functional jointimaging sequence at selected phases of leg extension from a seatedposition, D-F, anterior projection. The vectors represent the relativelocation and orientation of the femur with respect to the tibia. G-I isa lateral projection. These dynamic visualizations can be used todemonstrate tibiofemoral contact areas during various phases if gait orother physical activities.

Superimposition of Cartilage Thickness Map onto Subject SpecificAnatomic Model and Determination of Distance of Cartilage Defect fromLoad Bearing Line

Superimposing the cartilage thickness maps onto the subject specificgeometric models can follow the same approach taken to bring the MRgenerated geometries into the optical reference system. Since thethickness maps and the geometric models are initially in the samecoordinate system; one possible approach is to perform a simple surfacemapping of the thickness map onto the geometric model. Anotheralternative approach is to convert the thickness map directly into ageometric representation (FIG. 19).

Once the thickness map is embedded in the femoral geometry, one candefine a scalar metric that characterizes the location of any cartilagelesions relative to the point of contact line. One approach is a simple3D distance along the surface from the center of the cartilage lesion tothe point of closest approach of the contact line. Another metric thatcould be useful would be to multiply the area of the lesion by theadduction moment at that time instant, then divide by the distance fromlesion center to point of closest approach. This could result in ametric that increases with lesion area, adduction moment, and closenessof approach.

Display Correlated Images

Determination of Anatomic and Natural Reference Lines

There are two alternative approaches one can consider for determining areference line on the cartilage surfaces. One skilled in the art willeasily recognize other approaches that can be suitable for this purpose.The first approach is based on anatomic planes; the second is a naturalapproach building on the three dimensional cartilage thickness map.

The location of the pathway of loading relative to the femoral andtibial anatomy and geometry can be assessed by defining sagittal planesbisecting the medial femoral condyle, the lateral femoral condyle, themedial tibial plateau, and the lateral tibial plateau. For the medialfemoral condyle, the operator can manually delete surface points locatedalong the trochlea. Then, a sagittal plane parallel to the sagittalmidfemoral plane can be defined through the most medial aspect of themedial femoral condyle followed by a sagittal plane parallel to thesagittal midfemoral plane through the most lateral aspect of the medialfemoral condyle. The sagittal plane that is located halfway betweenthese two planes can be defined as the “midcondylar sagittal plane”. Theintersection between the midcondylar sagittal plane and the externalcartilage surface yields the “anatomic midcondylar cartilage line”. Thelocation of the pathway of loading can be assessed relative to theanatomic midcondylar cartilage line of the medial femoral condyle. Theidentical procedure can be repeated for the lateral femoral condyle.

The following method can be used for the medial tibial plateau: A planeparallel to the sagittal tibial plateau plane can be defined through themost medial point of the medial tibial plateau. A parallel plane locatedhalfway between this plane and the sagittal tibial plateau plane canyield the “midsagittal plane of the medial tibial plateau.” Theintersection of the midsagittal plane of the medial tibial plateau andthe external cartilage surface can yield the “anatomic midtibial plateaucartilage line” of the medial tibial plateau. The identical procedurecan be repeated for the lateral tibial plateau.

In the second approach, one can calculate a “natural” line of curvaturefor each femoral cartilage component (FIG. 20). Intuitively, if onecould roll the femoral condyles along a hard, flat surface, the line ofcontact with the flat surface would be the natural line of curvature.One can compare the actual tibiofemoral contact line to this referenceline. Since one cannot physically remove the femur and roll it around,one can apply some geometric calculations to estimate this referenceline. One can begin with the trans-epicondylar reference line previouslydescribed. One can then generate a plane coincident with this lineoriented in an arbitrary initial position. The intersection of thisplane and the external surface of the cartilage will produce a curve.One can then take the point furthest from the trans-epicondylarreference line as the natural contact point for this plane location. Thenext step is to rotate the plane by some increment, for example by onedegree, and repeat the procedure. The operator can identify the rotationangles where the plane is intersecting the distinct medial—lateralcompartments of the cartilage, and two points can be chosen, one fromthe medial femoral condyle and one from the lateral femoral condyle. Ifcartilage defects are present, in which case a compartment will notintersect in a curve but in a set of points, one can fit a splinethrough the points, then take the peak point of the spline as thecontact point.

This can be repeated for the entire extent of the cartilage, resultingin a set of points that branch at the intercondylar notch. One can treatthese points as two lines, and fit them with two splines. These can bethe “natural” lines of curvature for each compartment.

Load Bearing Line Determination

The calculations in this section can begin with the relative motion ofthe subject specific femoral anatomy with respect to the subjectspecific tibial anatomy, and end with a line describing the point ofclosest approach between the femur and tibia during some activity ofdaily living. A number of approaches to this problem have been describedin the literature; Crosset, Dennis, Stiehl, and Johnson have alldescribed techniques which might be applicable. One can implement aproximity detection and approach algorithm (PDAA) as it was specificallydesigned to work with the Point Cluster Technique (albeit withprosthetic knee joint components).

Physically, the tibial and femoral cartilage components deform underload, leading in general to a contact patch between opposing surfaces.As the geometric models are rigid, they will not deform under this load,but will instead intersect in a non-realizable manner. The PDAA has beendesigned to incrementally displace and rotate one of the surfaces untila realizable contact is achieved. It is understood that this is not atrue point contact line, but rather a reproducible representation ofcontact location (FIG. 21).

The MR generated subject specific geometries can be used to detect rigidbody contact proximity when the subject is in full extension. Thefemoral component can then be incrementally displaced until simultaneousmedial and lateral condyle contact occur. This is a first orderapproximation to the location of the contact point; slip velocitycalculations can then be used to determine the final estimate of thecontact point. The next time step in the activity can now be examined,using the previous time step solution as a starting point for thecalculation. The full extension time step can be chosen to match withthe static reference posture; should it be necessary, one can add inother reference postures.

Once the contact points have been determined for all time steps of theactivity, one can map the locations of these points onto the femoralcartilage. A coordinate system can be defined on the surface of thefemoral cartilage, choosing as a reference line the point of contact thefemoral component would have had were it rolled along a flat plane. Thisallows one to determine a contact line relative to the subject specificanatomy.

Assessing Cartilage Loss/Gain by Subtracting Images Acquired at TwoDifferent Times

Cartilage loss can be assessed by subtracting two images of the sameindividual that were acquired at different times t₁ and t₂. The sametechnique can be used to measure cartilage gain for example when apatient is undergoing chondro-regenerative treatment. Typically, theimages are three-dimensional data sets (e.g. magnetic resonance images)and will have similar contrast (e.g. cartilage is shown in bright, boneis shown in dark). For each voxel, the difference of the intensities(gray values) at times t₁ and t₂ is computed. The difference image isthen composed of the absolute values of the differences for each voxel.

The difference image cancels out structures that are the same in bothimages and enhances differences between the two images. This way,changes in the cartilage (loss or gain) are emphasized. In a subsequentstep, the enhanced voxels can be extracted (e.g. using a thresholding orseed growing technique) and counted. The number of extracted voxels,multiplied by the volume of each voxel, yields the volume of thecartilage loss or cartilage gain.

The following variations of the technique are possible, where two ormore variations can be combined:

The computation of the difference can be limited to a certain volume ofinterest (VOI) rather than the entire image, for example to assess thecartilage loss or gain at the site of a particular cartilage defect.

Prior to the calculation of the difference, a potential misalignment ofthe two images, that can for example be caused by differences in thepositioning of the patient in the image acquisition unit, can becompensated. Possible ways to achieve this are, for example,surface-based or volume-based registration methods.

Variations in the intensities of two corresponding structures in the twoimages can be compensated prior to calculating the difference image. Forexample, if cartilage in the image taken at t₁ has a lower intensitythan the cartilage in the image taken at t₂, the voxel gray values inthe first image can be adjusted so that they match the ones ofcorresponding structures in the second image.

Instead of calculating the absolute value of the differences for all thevoxels to create the difference image, only a certain range of thedifferences is considered. For example, only those voxel differencesthat are negative or only those that are positive are used in thecomposition of the difference image. This can help in differentiatingbetween a gain and a loss of cartilage

Provide Therapy

A 2D or 3D surface registration technique can be used as an aid toproviding therapy to match the anatomic orientation of the cartilagethickness map of a baseline and follow-up scan of a patient. There-registered cartilage thickness map of the follow-up scan can then besubtracted from the baseline scan. This will yield the thicknessdifference, i.e. cartilage loss, as a function of x, y, and z. This canalso be expressed as percentage difference.

The invention provides for techniques to assess biomechanical loadingconditions of articular cartilage in vivo using magnetic resonanceimaging and to use the assessment as an aid in providing therapy to apatient. In one embodiment, biomechanical loading conditions can beassessed in normal articular cartilage in various anatomic regions. Inthe knee joint, these anatomic regions include the posterior, central,and anterior medial femoral condyle, the posterior, central, andanterior medial tibial plateau, the posterior, central, and anteriorlateral femoral condyle, the posterior, central, and anterior lateraltibial plateau, the medial and lateral aspect of the trochlea, and themedial and lateral facet and the median ridge of the patella. Sincebiomechanical loading conditions are assessed in vivo based on theanatomic features of each individual patient, a risk profile can beestablished for each individual based on the biomechanical stressesapplied to cartilage. In this fashion, patients who are at risk fordeveloping early cartilage loss and osteoarthritis can be identified.For example, patients with a valgus or varus deformity of the knee jointwill demonstrate higher biomechanical stresses applied to the articularcartilage in the medial femorotibial or lateral femorotibial orpatellofemoral compartments than patients with normal joint anatomy.Similarly, patients with disturbances of joint congruity willdemonstrate higher biomechanical stress applied to certain regions ofthe articular cartilage. Such disturbances of joint congruity are oftendifficult to detect using standard clinical and imaging assessment. Theamount of stress applied to the articular cartilage can be used todetermine the patient's individual prognosis for developing cartilageloss and osteoarthritis. In another embodiment, biomechanical loadingconditions can be assessed in normal and diseased articular cartilage.An intervention that can alter load bearing can then be simulated. Suchinterventions include but are not limited to braces, orthotic devices,methods and devices to alter neuromuscular function or activation,arthroscopic and surgical procedures. The change in load bearing inducedby the intervention can be assessed prior to actually performing theintervention in a patient. In this fashion, the most efficacioustreatment modality can be determined. For example, a tibial osteotomycan be simulated in the manner and the optimal degree of angularcorrection with regard to biomechanical loading conditions of normal anddiseased cartilage can be determined before the patient will actuallyundergo surgery.

Estimation of biomechanical forces applied to normal cartilage can beused to determine a patient's risk for developing cartilage loss andosteoarthritis. Estimation of forces applied in and around a cartilagedefect can be used to determine the prognosis of a cartilage defect andto guide the choice of therapy, e.g. treatment with chondroprotective orchondroregenerative agents, osteochondral allografting, cartilagetransplantation, femoral or tibial osteotomy, or joint replacementsurgery.

Having now provided a full discussion of various aspects of thetechnology relating to this invention, several further aspects of theinvention can be seen.

One aspect of the invention is a method of assessing the condition of ajoint in a mammal. The method comprises:

(a) comparing the movement pattern of the joint with the cartilagedegeneration pattern of the joint; and

(b) determining the relationship between the movement pattern and thecartilage degeneration pattern

Another aspect of the invention is a method for monitoring the treatmentof a degenerative joint condition in a mammal. The method comprises

(a) comparing the movement pattern of the joint with the cartilagedegeneration pattern of the joint:

(b) determining the relationship between the movement pattern and thecartilage degeneration pattern;

(c) treating the mammal to minimize further degeneration of the jointcondition; and

(d) monitoring the treatment to the mammal.

Still another aspect of the invention is a method of assessing the rateof degeneration of cartilage in the joint of a mammal, wherein the jointcomprises cartilage and the bones on either side of the cartilage, whichmethod comprises

(a) obtaining a cartilage degeneration pattern of the joint that showsan area of greater than normal degeneration,

(b) obtaining a movement pattern of the joint that shows where theopposing cartilage surface contact,

(c) comparing the cartilage degeneration pattern with the movementpattern of the joint, and

(d) determining if the movement pattern shows contact of one cartilagesurface with a portion of the opposing cartilage surface showing greaterthan normal degeneration in the cartilage degeneration pattern.

Another aspect of the specification is a method for assessing thecondition of the knee joint of a human patient, wherein the knee jointcomprises cartilage and associated bones on either side of the joint.The method comprises

(a) obtaining the patient's magnetic resonance imaging (MRI) data of theknee showing at least the cartilage on at least one side of the joint,

(b) segmenting the MRI data from step (a),

(c) generating a geometrical or biochemical representation of thecartilage of the joint from the segmented MRI data,

(d) assessing the patient's gait to determine the cartilage surfacecontact pattern in the joint during the gait assessment, and

(e) correlating the contact pattern obtained in step (d) with thegeometrical representation obtained in step (c).

Still another aspect of this invention is a method for assessing thecondition of the knee joint of a human patient, wherein the knee jointcomprises cartilage and associated bones on either side of the joint.The method comprises

(a) obtaining the patient's magnetic resonance imaging (MRI) data of theknee showing at least the bones on either side of the joint,

(b) segmenting the MRI data from step (a),

(c) generating a geometrical representation of the bone of the jointfrom the segmented MRI data,

(d) assessing the patient's gait to determine the load pattern of thearticular cartilage in the joint during the gait assessment,

(e) correlating the load pattern obtained in step (d) with thegeometrical representation obtained in step (c).

Another aspect of this invention is a method for deriving the motion ofbones about a joint from markers placed on the skin, which methodcomprises

(a) placing at least three external markers on the patient's limbsegments surrounding the joint,

(b) registering the location of each marker on the patient's limb whilethe patient is standing completing still and while moving the limb,

(c) calculating the principal axis, principal moments and deformation ofrigidity of the cluster of markers, and

(d) calculating a correction to the artifact induced by the motion ofthe skin markers relative to the underlying bone.

Another aspect of the invention is a system for assessing the conditionof cartilage in a joint of a human, which system comprises

(a) a device for electronically transferring a cartilage degenerationpattern for the joint to receiving device located distant from thetransferring device;

(b) a device for receiving the cartilage degeneration pattern at theremote location;

(c) a database accessible at the remote location for generating amovement pattern for the joint of the human wherein the databaseincludes a collection of movement patterns for human joints, whichpatterns are organized and can be accessed by reference tocharacteristics such as type of joint, gender, age, height, weight, bonesize, type of movement, and distance of movement;

(d) a device for generating a movement pattern that most closelyapproximates a movement pattern for the human patient based on thecharacteristics of the human patient;

(e) a device for correlating the movement pattern with the cartilagedegeneration pattern; and

(f) a device for transmitting the correlated movement pattern with thecartilage degeneration pattern back to the source of the cartilagedegeneration pattern.

In each of these aspects of the invention it is to be understood that acartilage degeneration pattern may be, e.g., a 2D or 3D thickness map ofthe cartilage or a biochemical map of the cartilage.

All publications and patent applications mentioned in this specificationare herein incorporated by reference to the same extent as if eachindividual publication or patent application was. specifically andindividually indicated to be incorporated by reference.

The invention now being fully described, it will be apparent to one ofordinary skill in the art that many changes and modifications can bemade thereto without departing from the spirit or scope of the appendedclaims.

1. A method of assessing a joint disease, the method comprising:obtaining an electronic image of a joint; electronically evaluating saidimage to obtain at least one of volume, area, thickness, curvature,geometry, shape, biochemical contents, signal intensity and relaxationtime of normal and/or diseased tissue; and determining one or more axesrelated to said joint.
 2. The method according to claim 1, wherein saidaxis is an anatomic axis.
 3. The method according to claim 1, whereinsaid axis is a biomechanical axis.
 4. The method according to claim 1,wherein obtaining an electronic image includes performing an MRI.
 5. Themethod according to claim 1, wherein obtaining an electronic imageincludes performing a CT.
 6. The method according to claim 5, whereinperforming said CT includes performing a spiral CT.
 7. The methodaccording to claim 1, wherein obtaining an electronic image includesperforming an ultrasound.
 8. The method according to claim 1, whereinobtaining an electronic image includes performing an x-ray.
 9. Themethod according to claim 1, wherein obtaining an electronic imageincludes using a contrast agent.
 10. The method according to claim 1,further comprising creating at least one of a cast or physical model ormold of all or portions of said joint.
 11. The method of claim 1,further comprising guiding a treatment as a function of saidelectronically evaluating and said determining an axis.
 12. The methodof claim 10, wherein said guiding includes optimizing the alignment of atreatment.
 13. The method of claim 11, wherein said optimizing of thealignment optimizes biomechanical forces applied to the joint.
 14. Amethod of assessing a joint disease, the method comprising: obtaining anelectronic image of a joint; electronically evaluating said image toobtain at least one of volume, area, thickness, curvature, geometry,shape, biochemical contents, signal intensity and relaxation time ofnormal and/or diseased tissue; creating at least one of a cast, aphysical model, and a mold of all or a portion of said joint; andevaluating one or more movement patterns of said joint.
 15. The methodof claim 14, wherein said evaluating of said movement pattern includestesting said joint in at least one or more flexion angles.
 16. Themethod of claim 14, wherein said evaluating of said movement patternincludes testing said joint in at least one or more extension angles.17. The method of claim 14, wherein said evaluating of said movementpattern includes testing said joint in at least one or more abductionangles.
 18. The method of claim 14, wherein said evaluating of saidmovement pattern includes testing said joint in at least one or moreadduction angles.
 19. The method of claim 14, wherein said evaluating ofsaid movement pattern includes testing said joint in at least one ormore rotation angles.
 20. A method of assessing a joint disease, themethod comprising: obtaining an electronic image of a joint;electronically evaluating said image to obtain at least one of volume,area, thickness, curvature, geometry, shape, biochemical contents,signal intensity and relaxation time of normal and/or diseasedcartilage; and creating at least one of a cast, physical model, and amold of all or a portion of said joint.
 21. A method of assessing ajoint disease, the method comprising: obtaining an electronic image of ajoint; electronically evaluating said image to obtain at least one ofvolume, area, thickness, curvature, geometry, shape, biochemicalcontents, signal intensity and relaxation time of normal and/or diseasedcartilage and subchondral bone; and creating at least one of a cast, aphysical model, and a mold of all or a portion of said joint.
 22. Amethod of assessing a joint disease, the method comprising: obtaining anelectronic image of a joint; electronically evaluating said image toobtain at least one of volume, area, thickness, curvature, geometry,shape, biochemical contents, signal intensity or relaxation time of saidjoint; segmenting cartilage or subchondral bone associated with thejoint; and creating at least one of a cast, physical model and mold ofall or a portion of said joint.
 23. The method of claim 22, wherein saidsegmention includes using at least one of an active contour algorithm, alivewire algorithm, thresholding, a seed growing algorithm, a texturebased algorithm and a model based segmentation algorithm.